Dataset name: .
The dataset has N=207 rows and 35 columns. 0 rows have no missing values on any column.
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#Variables
Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event.
## No non-missing values to show.
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|
| sikker_fg | Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event. | haven_labelled | 207 | 0 | Inf | NA | -Inf | 2 | F40.0 | 5 |
| name | value |
|---|---|
| Jeg vil delta i undersøkelsen | 1 |
| Jeg samtykker ikke og vil ikke delta i undersøkelsen eller det påfølgende arrangementet | 2 |
First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence
Distribution of values for fg_kunnskap_1
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap_1 | First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence | haven_labelled | 0 | 1 | 1 | 4 | 6 | 3.73913 | 0.7692925 | 6 | ▁▁▁▂▇▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very good knowledge | 1 |
| Good knowledge | 2 |
| Somwhat good knowledge | 3 |
| Little knowledge | 4 |
| No knowledge at all | 5 |
| Do not know | 6 |
First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway
Distribution of values for fg_kunnskap_2
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap_2 | First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway | haven_labelled | 0 | 1 | 1 | 4 | 6 | 4.019324 | 0.9188498 | 6 | ▁▁▁▂▇▁▅▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very good knowledge | 1 |
| Good knowledge | 2 |
| Somwhat good knowledge | 3 |
| Little knowledge | 4 |
| No knowledge at all | 5 |
| Do not know | 6 |
First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work
Distribution of values for fg_kunnskap_3
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap_3 | First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work | haven_labelled | 0 | 1 | 1 | 4 | 6 | 3.961353 | 1.060525 | 6 | ▁▂▁▃▇▁▆▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very good knowledge | 1 |
| Good knowledge | 2 |
| Somwhat good knowledge | 3 |
| Little knowledge | 4 |
| No knowledge at all | 5 |
| Do not know | 6 |
Based on what you know, do you think the Norwegian authorities’ use of machine learning and artificial intelligence will lead to an improvement or worsening of public services?
Distribution of values for fg_forbedring
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_forbedring | Based on what you know, do you think the Norwegian authorities’ use of machine learning and artificial intelligence will lead to an improvement or worsening of public services? | haven_labelled | 0 | 1 | 1 | 3 | 9 | 4.091787 | 2.2396 | 8 | ▃▇▂▂▁▁▁▃ | F40.0 | 5 |
| name | value |
|---|---|
| Very strong improvement | 1 |
| Strong improvement | 2 |
| Some improvement | 3 |
| Neither improvement or worsening | 4 |
| Some worsening | 5 |
| Strong worsening | 6 |
| Very strong worsening | 7 |
| Do not know | 9 |
And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it
Distribution of values for fg_likhet_1
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_likhet_1 | And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it | haven_labelled | 0 | 1 | 1 | 5 | 8 | 4.797101 | 2.001793 | 7 | ▁▂▇▆▆▃▂▆ | F40.0 | 5 |
| name | value |
|---|---|
| Very much more | 1 |
| More | 2 |
| Somwhat more | 3 |
| Neither more or less | 4 |
| Less | 6 |
| Very much less | 7 |
| Do not know | 8 |
And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector
Distribution of values for fg_likhet_2
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_likhet_2 | And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector | haven_labelled | 0 | 1 | 1 | 3 | 8 | 3.652174 | 1.914376 | 8 | ▁▆▇▃▂▁▁▂ | F40.0 | 5 |
| name | value |
|---|---|
| Very much more | 1 |
| More | 2 |
| Somwhat more | 3 |
| Neither more or less | 4 |
| Somewhat less | 5 |
| Less | 6 |
| Very much less | 7 |
| Do not know | 8 |
And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities’ decisions
Distribution of values for fg_likhet_3
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_likhet_3 | And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities’ decisions | haven_labelled | 0 | 1 | 1 | 4 | 8 | 4.729469 | 1.763781 | 8 | ▁▁▃▇▃▂▁▃ | F40.0 | 5 |
| name | value |
|---|---|
| Very much more | 1 |
| More | 2 |
| Somwhat more | 3 |
| Neither more or less | 4 |
| Somewhat less | 5 |
| Less | 6 |
| Very much less | 7 |
| Do not know | 8 |
Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees?
Distribution of values for fg_kunnskap2
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap2 | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees? | haven_labelled | 0 | 1 | 1 | 4 | 8 | 4.36715 | 1.376108 | 8 | ▁▂▅▇▇▂▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Oppose very strongly | 1 |
| Oppose strongly | 2 |
| Oppose somewhat | 3 |
| Neither oppose or support | 4 |
| Support somewhat | 5 |
| Support strongly | 6 |
| Support very stringly | 7 |
| Do not know | 8 |
Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer?
Distribution of values for fg_auto_flykt
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_auto_flykt | Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 0 | 1 | 1 | 1 | 4 | 1.328502 | 0.7747904 | 3 | ▇▁▂▁▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 4 |
Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities?
Distribution of values for fg_region
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_region | Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities? | haven_labelled | 0 | 1 | 1 | 4 | 8 | 4.560386 | 1.531221 | 8 | ▁▂▃▇▇▃▁▂ | F40.0 | 5 |
| name | value |
|---|---|
| Oppose very strongly | 1 |
| Oppose strongly | 2 |
| Oppose somewhat | 3 |
| Neither oppose or support | 4 |
| Support somewhat | 5 |
| Support strongly | 6 |
| Support very stringly | 7 |
| Do not know | 8 |
Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison?
Distribution of values for fg_kjennskap_prv
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kjennskap_prv | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison? | haven_labelled | 0 | 1 | 1 | 3 | 8 | 3.695652 | 1.784271 | 8 | ▃▅▇▃▇▂▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Oppose very strongly | 1 |
| Oppose strongly | 2 |
| Oppose somewhat | 3 |
| Neither oppose or support | 4 |
| Support somewhat | 5 |
| Support strongly | 6 |
| Support very stringly | 7 |
| Do not know | 8 |
Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer?
Distribution of values for fg_auto_prv
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_auto_prv | Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 0 | 1 | 1 | 1 | 3 | 1.120773 | 0.4061462 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 3 |
How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees
Distribution of values for fg_imp_1
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_imp_1 | How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees | haven_labelled | 0 | 1 | 2 | 4 | 6 | 4.004831 | 0.766391 | 6 | ▁▃▁▇▁▃▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Not important at all | 1 |
| Not important | 2 |
| Somwhat important | 3 |
| Very important | 4 |
| Extremely improtant | 5 |
| Do not know | 6 |
How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting
Distribution of values for fg_imp_2
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_imp_2 | How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting | haven_labelled | 0 | 1 | 1 | 4 | 6 | 3.869565 | 0.8345636 | 6 | ▁▁▁▃▇▁▂▁ | F40.0 | 5 |
| name | value |
|---|---|
| Not important at all | 1 |
| Not important | 2 |
| Somwhat important | 3 |
| Very important | 4 |
| Extremely improtant | 5 |
| Do not know | 6 |
How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates
Distribution of values for fg_imp_3
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_imp_3 | How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates | haven_labelled | 0 | 1 | 2 | 5 | 6 | 4.434783 | 0.7532118 | 6 | ▁▂▁▅▁▇▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Not important at all | 1 |
| Not important | 2 |
| Somwhat important | 3 |
| Very important | 4 |
| Extremely improtant | 5 |
| Do not know | 6 |
Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen?
Distribution of values for fg_flykt_post
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_flykt_post | Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen? | haven_labelled | 0 | 1 | 1 | 2 | 3 | 1.719807 | 0.5385561 | 3 | ▅▁▁▇▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 3 |
Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen?
Distribution of values for fg_prv_post
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_prv_post | Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen? | haven_labelled | 0 | 1 | 1 | 2 | 3 | 1.652174 | 0.6860486 | 3 | ▇▁▁▇▁▁▁▂ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 3 |
Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer?
Distribution of values for fg_tradeoff_1
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_tradeoff_1 | Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer? | haven_labelled | 0 | 1 | 1 | 1 | 3 | 1.657005 | 0.7840873 | 3 | ▇▁▁▅▁▁▁▃ | F40.0 | 5 |
| name | value |
|---|---|
| A | 1 |
| B | 2 |
| Do not know | 3 |
Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two
Distribution of values for fg_tradeoff_2
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_tradeoff_2 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two | haven_labelled | 0 | 1 | 1 | 2 | 3 | 1.975845 | 0.8614709 | 3 | ▇▁▁▆▁▁▁▇ | F40.0 | 5 |
| name | value |
|---|---|
| X | 1 |
| Y | 2 |
| Do not know | 3 |
Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive
Distribution of values for fg_tradeoff_3
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_tradeoff_3 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive | haven_labelled | 1 | 0.9951691 | 1 | 2 | 3 | 1.985437 | 0.7869556 | 3 | ▆▁▁▇▁▁▁▆ | F40.0 | 5 |
| name | value |
|---|---|
| One | 1 |
| Two | 2 |
| Do not know | 3 |
What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you.
Distribution of values for demo_edu
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_edu | What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you. | haven_labelled | 0 | 1 | 2 | 6 | 9 | 5.560386 | 1.571894 | 7 | ▁▅▁▃▇▇▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| No eductation | 1 |
| Pre-school | 2 |
| High school | 3 |
| Vocational school level (includes educations that are based on upper secondary school, but which are not approved as university and college education) | 5 |
| University or collage, up to 4 yearrs | 6 |
| University or collage, more than 4 yearrs | 7 |
| Other | 9 |
Which party would you vote for if there were a parliamentary election tomorrow?
Distribution of values for demo_party
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_party | Which party would you vote for if there were a parliamentary election tomorrow? | haven_labelled | 1 | 0.9951691 | 1 | 6 | 13 | 6.145631 | 3.683603 | 12 | ▇▃▁▇▃▂▂▃ | F40.0 | 5 |
| name | value |
|---|---|
| Progress party | 1 |
| Conservative party | 2 |
| Liberal Party | 3 |
| Christian democrats | 4 |
| Green party | 13 |
| Center party | 5 |
| Labour party | 6 |
| Socialist party | 7 |
| Red party | 8 |
| Other | 9 |
| Could not vote | 10 |
| Would not vote | 11 |
How much trust or distrust do you have in scientists?
Distribution of values for demo_trust
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_trust | How much trust or distrust do you have in scientists? | haven_labelled | 0 | 1 | 1 | 2 | 7 | 2.386473 | 1.072793 | 8 | ▂▇▂▂▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very high trust | 1 |
| High trust | 2 |
| Some trust | 3 |
| Neither trust nor mistrust | 4 |
| Some mistrust | 5 |
| High mistrust | 6 |
| Very high mistrust | 7 |
| Do not know | 8 |
In politics, you often talk about the ‘left’ and the ‘right’. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale?
Distribution of values for demo_lr_scale
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_lr_scale | In politics, you often talk about the ‘left’ and the ‘right’. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale? | haven_labelled | 1 | 0.9951691 | 0 | 5 | 11 | 5.165049 | 2.79755 | 12 | ▂▃▇▆▃▆▁▃ | F40.0 | 5 |
| name | value |
|---|---|
| 0 - Left | 0 |
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
| 4 | 4 |
| 5 | 5 |
| 6 | 6 |
| 7 | 7 |
| 8 | 8 |
| 9 | 9 |
| 10 | 10 |
| 11- Right | 11 |
0 == Pre deliberation survey responses, 1 == post delibiration survey
Distribution of values for post
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| post | 0 == Pre deliberation survey responses, 1 == post delibiration survey | numeric | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ▁▁▇▁▁ |
Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation?
## No non-missing values to show.
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_sikkert_post | Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation? | haven_labelled | 207 | 0 | Inf | NA | -Inf | 3 | F40.0 | 5 |
| name | value |
|---|---|
| Veldig negativ | 1 |
| Veldig negativ | 2 |
| Veldig negativ | 3 |
Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts?
## No non-missing values to show.
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|
| fo_delib | Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts? | haven_labelled | 207 | 0 | Inf | NA | -Inf | 4 | F40.0 | 5 |
| name | value |
|---|---|
| Veldig negativ | 1 |
| Veldig negativ | 2 |
| Veldig negativ | 3 |
| Veldig negativ | 4 |
Vi takker så mye for din deltakelse!
Helt til slutt: Det kan hende noen media ønsker å komme i kontakt med enkelte deltakere for å høre om deres opplevelser med å delta i dette arrangementet. Dersom det skulle komme en slik henvendelse, vil du være villig til at de tar kontakt med deg?
Svaret ditt er ikke bindende.
## No non-missing values to show.
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|
| fo_media | Vi takker så mye for din deltakelse! |
Helt til slutt: Det kan hende noen media ønsker å komme i
kontakt med enkelte deltakere for å høre om deres opplevelser med å
delta i dette arrangementet. Dersom det skulle komme en slik
henvendelse, vil du være villig til at de tar kontakt med deg?
Svaret ditt er ikke bindende. |haven_labelled | 207| 0|Inf |NA |-Inf
| 2| |F40.0 |5 |
| name | value |
|---|---|
| Veldig negativ | 4 |
| Veldig negativ | 5 |
Føler du deg mer sikker eller mer usikker i ditt syn på ulike metoder for å fjerne CO2 fra luften etter at du deltok i deliberasjonen?
## No non-missing values to show.
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|
| on_sikkert_post | Føler du deg mer sikker eller mer usikker i ditt syn på ulike metoder for å fjerne CO2 fra luften etter at du deltok i deliberasjonen? | haven_labelled | 207 | 0 | Inf | NA | -Inf | 3 | F40.0 | 5 |
| name | value |
|---|---|
| Veldig negativ | 1 |
| Veldig negativ | 2 |
| Veldig negativ | 3 |
1 == Treatment group (AI), 0 == control group (carbon capture)
Distribution of values for fg
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| fg | 1 == Treatment group (AI), 0 == control group (carbon capture) | numeric | 0 | 1 | 0 | 1 | 1 | 0.5700483 | 0.4962691 | ▆▁▁▁▇ |
| name | label | data_type | value_labels | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sikker_fg | Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event. | haven_labelled | 1. Jeg vil delta i undersøkelsen, 2. Jeg samtykker ikke og vil ikke delta i undersøkelsen eller det påfølgende arrangementet |
207 | 0.0000000 | Inf | NA | -Inf | NaN | NA | 2 | F40.0 | 5 | |
| fg_kunnskap_1 | First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence | haven_labelled | 1. Very good knowledge, 2. Good knowledge, 3. Somwhat good knowledge, 4. Little knowledge, 5. No knowledge at all, 6. Do not know |
0 | 1.0000000 | 1 | 4 | 6 | 3.7391304 | 0.7692925 | 6 | ▁▁▁▂▇▁▁▁ | F40.0 | 5 |
| fg_kunnskap_2 | First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway | haven_labelled | 1. Very good knowledge, 2. Good knowledge, 3. Somwhat good knowledge, 4. Little knowledge, 5. No knowledge at all, 6. Do not know |
0 | 1.0000000 | 1 | 4 | 6 | 4.0193237 | 0.9188498 | 6 | ▁▁▁▂▇▁▅▁ | F40.0 | 5 |
| fg_kunnskap_3 | First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work | haven_labelled | 1. Very good knowledge, 2. Good knowledge, 3. Somwhat good knowledge, 4. Little knowledge, 5. No knowledge at all, 6. Do not know |
0 | 1.0000000 | 1 | 4 | 6 | 3.9613527 | 1.0605247 | 6 | ▁▂▁▃▇▁▆▁ | F40.0 | 5 |
| fg_forbedring | Based on what you know, do you think the Norwegian authorities’ use of machine learning and artificial intelligence will lead to an improvement or worsening of public services? | haven_labelled | 1. Very strong improvement, 2. Strong improvement, 3. Some improvement, 4. Neither improvement or worsening, 5. Some worsening, 6. Strong worsening, 7. Very strong worsening, 9. Do not know |
0 | 1.0000000 | 1 | 3 | 9 | 4.0917874 | 2.2395995 | 8 | ▃▇▂▂▁▁▁▃ | F40.0 | 5 |
| fg_likhet_1 | And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it | haven_labelled | 1. Very much more, 2. More, 3. Somwhat more, 4. Neither more or less, 6. Less, 7. Very much less, 8. Do not know |
0 | 1.0000000 | 1 | 5 | 8 | 4.7971014 | 2.0017932 | 7 | ▁▂▇▆▆▃▂▆ | F40.0 | 5 |
| fg_likhet_2 | And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector | haven_labelled | 1. Very much more, 2. More, 3. Somwhat more, 4. Neither more or less, 5. Somewhat less, 6. Less, 7. Very much less, 8. Do not know |
0 | 1.0000000 | 1 | 3 | 8 | 3.6521739 | 1.9143765 | 8 | ▁▆▇▃▂▁▁▂ | F40.0 | 5 |
| fg_likhet_3 | And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities’ decisions | haven_labelled | 1. Very much more, 2. More, 3. Somwhat more, 4. Neither more or less, 5. Somewhat less, 6. Less, 7. Very much less, 8. Do not know |
0 | 1.0000000 | 1 | 4 | 8 | 4.7294686 | 1.7637810 | 8 | ▁▁▃▇▃▂▁▃ | F40.0 | 5 |
| fg_kunnskap2 | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
0 | 1.0000000 | 1 | 4 | 8 | 4.3671498 | 1.3761079 | 8 | ▁▂▅▇▇▂▁▁ | F40.0 | 5 |
| fg_auto_flykt | Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 4. Do not know |
0 | 1.0000000 | 1 | 1 | 4 | 1.3285024 | 0.7747904 | 3 | ▇▁▂▁▁▁▁▁ | F40.0 | 5 |
| fg_region | Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
0 | 1.0000000 | 1 | 4 | 8 | 4.5603865 | 1.5312207 | 8 | ▁▂▃▇▇▃▁▂ | F40.0 | 5 |
| fg_kjennskap_nav | Based on your knowledge, to what extent do you support that NAV should be open to using artificial intelligence when deciding who should be invited to a dialogue meeting? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
0 | 1.0000000 | 1 | 5 | 8 | 4.3816425 | 1.6588249 | 8 | ▁▂▅▃▇▂▁▁ | F40.0 | 5 |
| fg_auto_nav | Imagine two situations. In one situation, a case manager will invite to a dialogue meeting based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
0 | 1.0000000 | 1 | 1 | 3 | 1.2463768 | 0.5234074 | 3 | ▇▁▁▂▁▁▁▁ | F40.0 | 5 |
| fg_pari_nav | Machine learning involves getting computers to learn to solve tasks based on data material. A dilemma that often arises is that different algorithms have different advantages and disadvantages, and you have to choose which considerations to prioritize. Imagine that NAV has to choose between using one of these two algorithms: The first algorithm is most accurate. Among those who need a dialogue meeting, however, more [Field-fairgov_pari_treat] are offered a dialogue meeting, even though there are an equal number of women and men who are on sick leave and need a dialogue meeting. The proportion who need a dialogue meeting without receiving an offer is therefore larger at [Field-fairgov_pari_treat_2]. The second algorithm ensures that the proportion of those on sick leave who are called to a dialogue meeting is equal for women and men. However, it is less accurate overall, so that fewer people who need a dialogue meeting are called. This applies to both women and men. If it was only between these two, which one do you think seems more fair? | haven_labelled | 1. The first algorithm seems most fair, 2. The second algorithm seems most fair, 3. Do not know |
0 | 1.0000000 | 1 | 1 | 3 | 1.6038647 | 0.7357610 | 3 | ▇▁▁▅▁▁▁▂ | F40.0 | 5 |
| fg_kjennskap_prv | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
0 | 1.0000000 | 1 | 3 | 8 | 3.6956522 | 1.7842707 | 8 | ▃▅▇▃▇▂▁▁ | F40.0 | 5 |
| fg_auto_prv | Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
0 | 1.0000000 | 1 | 1 | 3 | 1.1207729 | 0.4061462 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| fg_imp_1 | How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees | haven_labelled | 1. Not important at all, 2. Not important, 3. Somwhat important, 4. Very important, 5. Extremely improtant, 6. Do not know |
0 | 1.0000000 | 2 | 4 | 6 | 4.0048309 | 0.7663910 | 6 | ▁▃▁▇▁▃▁▁ | F40.0 | 5 |
| fg_imp_2 | How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting | haven_labelled | 1. Not important at all, 2. Not important, 3. Somwhat important, 4. Very important, 5. Extremely improtant, 6. Do not know |
0 | 1.0000000 | 1 | 4 | 6 | 3.8695652 | 0.8345636 | 6 | ▁▁▁▃▇▁▂▁ | F40.0 | 5 |
| fg_imp_3 | How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates | haven_labelled | 1. Not important at all, 2. Not important, 3. Somwhat important, 4. Very important, 5. Extremely improtant, 6. Do not know |
0 | 1.0000000 | 2 | 5 | 6 | 4.4347826 | 0.7532118 | 6 | ▁▂▁▅▁▇▁▁ | F40.0 | 5 |
| fg_flykt_post | Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
0 | 1.0000000 | 1 | 2 | 3 | 1.7198068 | 0.5385561 | 3 | ▅▁▁▇▁▁▁▁ | F40.0 | 5 |
| fg_nav_post | Case 2 of 3: Dialogue meeting with NAV As sick leave, you receive follow-up from NAV. They will assess whether you should be invited to a dialogue meeting to discuss what can be done to enable you to return to work. Dialogue meetings require resources, so not everyone can get that offer. When NAV assesses your need, you get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_di_treat] will take much more time than the algorithm, which extends the period accordingly before you possibly receive an invitation to a dialogue meeting. Which decision-making process would you have chosen? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
0 | 1.0000000 | 1 | 2 | 3 | 1.5942029 | 0.5990002 | 3 | ▇▁▁▇▁▁▁▁ | F40.0 | 5 |
| fg_prv_post | Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
0 | 1.0000000 | 1 | 2 | 3 | 1.6521739 | 0.6860486 | 3 | ▇▁▁▇▁▁▁▂ | F40.0 | 5 |
| fg_tradeoff_1 | Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer? | haven_labelled | 1. A, 2. B, 3. Do not know |
0 | 1.0000000 | 1 | 1 | 3 | 1.6570048 | 0.7840873 | 3 | ▇▁▁▅▁▁▁▃ | F40.0 | 5 |
| fg_tradeoff_2 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two | haven_labelled | 1. X, 2. Y, 3. Do not know |
0 | 1.0000000 | 1 | 2 | 3 | 1.9758454 | 0.8614709 | 3 | ▇▁▁▆▁▁▁▇ | F40.0 | 5 |
| fg_tradeoff_3 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive | haven_labelled | 1. One, 2. Two, 3. Do not know |
1 | 0.9951691 | 1 | 2 | 3 | 1.9854369 | 0.7869556 | 3 | ▆▁▁▇▁▁▁▆ | F40.0 | 5 |
| demo_edu | What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you. | haven_labelled | 1. No eductation, 2. Pre-school, 3. High school, 5. Vocational school level (includes educations that are based on upper secondary school, but which are not approved as university and college education), 6. University or collage, up to 4 yearrs, 7. University or collage, more than 4 yearrs, 9. Other |
0 | 1.0000000 | 2 | 6 | 9 | 5.5603865 | 1.5718939 | 7 | ▁▅▁▃▇▇▁▁ | F40.0 | 5 |
| demo_party | Which party would you vote for if there were a parliamentary election tomorrow? | haven_labelled | 1. Progress party, 2. Conservative party, 3. Liberal Party, 4. Christian democrats, 13. Green party, 5. Center party, 6. Labour party, 7. Socialist party, 8. Red party, 9. Other, 10. Could not vote, 11. Would not vote |
1 | 0.9951691 | 1 | 6 | 13 | 6.1456311 | 3.6836031 | 12 | ▇▃▁▇▃▂▂▃ | F40.0 | 5 |
| demo_trust | How much trust or distrust do you have in scientists? | haven_labelled | 1. Very high trust, 2. High trust, 3. Some trust, 4. Neither trust nor mistrust, 5. Some mistrust, 6. High mistrust, 7. Very high mistrust, 8. Do not know |
0 | 1.0000000 | 1 | 2 | 7 | 2.3864734 | 1.0727927 | 8 | ▂▇▂▂▁▁▁▁ | F40.0 | 5 |
| demo_lr_scale | In politics, you often talk about the ‘left’ and the ‘right’. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale? | haven_labelled | 0. 0 - Left, 1. 1, 2. 2, 3. 3, 4. 4, 5. 5, 6. 6, 7. 7, 8. 8, 9. 9, 10. 10, 11. 11- Right |
1 | 0.9951691 | 0 | 5 | 11 | 5.1650485 | 2.7975498 | 12 | ▂▃▇▆▃▆▁▃ | F40.0 | 5 |
| post | 0 == Pre deliberation survey responses, 1 == post delibiration survey | numeric | . | 0 | 1.0000000 | 0 | 0 | 0 | 0.0000000 | 0.0000000 | NA | ▁▁▇▁▁ | NA | NA |
| fg_sikkert_post | Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation? | haven_labelled | 1. Veldig negativ, 2. Veldig negativ, 3. Veldig negativ |
207 | 0.0000000 | Inf | NA | -Inf | NaN | NA | 3 | F40.0 | 5 | |
| fo_delib | Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts? | haven_labelled | 1. Veldig negativ, 2. Veldig negativ, 3. Veldig negativ, 4. Veldig negativ |
207 | 0.0000000 | Inf | NA | -Inf | NaN | NA | 4 | F40.0 | 5 | |
| fo_media | Vi takker så mye for din deltakelse! |
Helt til slutt: Det kan hende noen media ønsker å komme i
kontakt med enkelte deltakere for å høre om deres opplevelser med å
delta i dette arrangementet. Dersom det skulle komme en slik
henvendelse, vil du være villig til at de tar kontakt med deg?
Svaret ditt er ikke bindende. |haven_labelled |4. Veldig
negativ,
5. Veldig negativ | 207| 0.0000000|Inf |NA |-Inf | NaN| NA|
2| |F40.0 |5 | |on_sikkert_post |Føler du
deg mer sikker eller mer usikker i ditt syn på ulike metoder for å
fjerne CO2 fra luften etter at du deltok i deliberasjonen?
|haven_labelled |1. Veldig negativ,
2. Veldig negativ,
3. Veldig
negativ | 207| 0.0000000|Inf |NA |-Inf | NaN| NA| 3| |F40.0 |5 |
|fg |1 == Treatment group (AI), 0 == control group
(carbon capture) |numeric |. | 0| 1.0000000|0 |1 |1 | 0.5700483|
0.4962691| NA|▆▁▁▁▇ |NA |NA |
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": ".",
"datePublished": "2022-11-16",
"description": "The dataset has N=207 rows and 35 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
"keywords": ["sikker_fg", "fg_kunnskap_1", "fg_kunnskap_2", "fg_kunnskap_3", "fg_forbedring", "fg_likhet_1", "fg_likhet_2", "fg_likhet_3", "fg_kunnskap2", "fg_auto_flykt", "fg_region", "fg_kjennskap_nav", "fg_auto_nav", "fg_pari_nav", "fg_kjennskap_prv", "fg_auto_prv", "fg_imp_1", "fg_imp_2", "fg_imp_3", "fg_flykt_post", "fg_nav_post", "fg_prv_post", "fg_tradeoff_1", "fg_tradeoff_2", "fg_tradeoff_3", "demo_edu", "demo_party", "demo_trust", "demo_lr_scale", "post", "fg_sikkert_post", "fo_delib", "fo_media", "on_sikkert_post", "fg"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "sikker_fg",
"description": "Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event.",
"value": "1. Jeg vil delta i undersøkelsen,\n2. Jeg samtykker ikke og vil ikke delta i undersøkelsen eller det påfølgende arrangementet",
"maxValue": 2,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap_1",
"description": "First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence",
"value": "1. Very good knowledge,\n2. Good knowledge,\n3. Somwhat good knowledge,\n4. Little knowledge,\n5. No knowledge at all,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap_2",
"description": "First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway",
"value": "1. Very good knowledge,\n2. Good knowledge,\n3. Somwhat good knowledge,\n4. Little knowledge,\n5. No knowledge at all,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap_3",
"description": "First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work",
"value": "1. Very good knowledge,\n2. Good knowledge,\n3. Somwhat good knowledge,\n4. Little knowledge,\n5. No knowledge at all,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_forbedring",
"description": "Based on what you know, do you think the Norwegian authorities' use of machine learning and artificial intelligence will lead to an improvement or worsening of public services?",
"value": "1. Very strong improvement,\n2. Strong improvement,\n3. Some improvement,\n4. Neither improvement or worsening,\n5. Some worsening,\n6. Strong worsening,\n7. Very strong worsening,\n9. Do not know",
"maxValue": 9,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_likhet_1",
"description": "And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it",
"value": "1. Very much more,\n2. More,\n3. Somwhat more,\n4. Neither more or less,\n6. Less,\n7. Very much less,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_likhet_2",
"description": "And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector",
"value": "1. Very much more,\n2. More,\n3. Somwhat more,\n4. Neither more or less,\n5. Somewhat less,\n6. Less,\n7. Very much less,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_likhet_3",
"description": "And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities' decisions",
"value": "1. Very much more,\n2. More,\n3. Somwhat more,\n4. Neither more or less,\n5. Somewhat less,\n6. Less,\n7. Very much less,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap2",
"description": "Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_auto_flykt",
"description": "Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n4. Do not know",
"maxValue": 4,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_region",
"description": "Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kjennskap_nav",
"description": "Based on your knowledge, to what extent do you support that NAV should be open to using artificial intelligence when deciding who should be invited to a dialogue meeting?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_auto_nav",
"description": "Imagine two situations. In one situation, a case manager will invite to a dialogue meeting based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_pari_nav",
"description": "Machine learning involves getting computers to learn to solve tasks based on data material. A dilemma that often arises is that different algorithms have different advantages and disadvantages, and you have to choose which considerations to prioritize. Imagine that NAV has to choose between using one of these two algorithms: The first algorithm is most accurate. Among those who need a dialogue meeting, however, more [Field-fairgov_pari_treat] are offered a dialogue meeting, even though there are an equal number of women and men who are on sick leave and need a dialogue meeting. The proportion who need a dialogue meeting without receiving an offer is therefore larger at [Field-fairgov_pari_treat_2]. The second algorithm ensures that the proportion of those on sick leave who are called to a dialogue meeting is equal for women and men. However, it is less accurate overall, so that fewer people who need a dialogue meeting are called. This applies to both women and men. If it was only between these two, which one do you think seems more fair?",
"value": "1. The first algorithm seems most fair,\n2. The second algorithm seems most fair,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kjennskap_prv",
"description": "Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_auto_prv",
"description": "Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_imp_1",
"description": "How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees",
"value": "1. Not important at all,\n2. Not important,\n3. Somwhat important,\n4. Very important,\n5. Extremely improtant,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_imp_2",
"description": "How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting",
"value": "1. Not important at all,\n2. Not important,\n3. Somwhat important,\n4. Very important,\n5. Extremely improtant,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_imp_3",
"description": "How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates",
"value": "1. Not important at all,\n2. Not important,\n3. Somwhat important,\n4. Very important,\n5. Extremely improtant,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_flykt_post",
"description": "Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_nav_post",
"description": "Case 2 of 3: Dialogue meeting with NAV As sick leave, you receive follow-up from NAV. They will assess whether you should be invited to a dialogue meeting to discuss what can be done to enable you to return to work. Dialogue meetings require resources, so not everyone can get that offer. When NAV assesses your need, you get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_di_treat] will take much more time than the algorithm, which extends the period accordingly before you possibly receive an invitation to a dialogue meeting. Which decision-making process would you have chosen?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_prv_post",
"description": "Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_tradeoff_1",
"description": "Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer?",
"value": "1. A,\n2. B,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_tradeoff_2",
"description": "Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two",
"value": "1. X,\n2. Y,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_tradeoff_3",
"description": "Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive",
"value": "1. One,\n2. Two,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_edu",
"description": "What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you.",
"value": "1. No eductation,\n2. Pre-school,\n3. High school,\n5. Vocational school level (includes educations that are based on upper secondary school, but which are not approved as university and college education),\n6. University or collage, up to 4 yearrs,\n7. University or collage, more than 4 yearrs,\n9. Other",
"maxValue": 9,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_party",
"description": "Which party would you vote for if there were a parliamentary election tomorrow?",
"value": "1. Progress party,\n2. Conservative party,\n3. Liberal Party,\n4. Christian democrats,\n13. Green party,\n5. Center party,\n6. Labour party,\n7. Socialist party,\n8. Red party,\n9. Other,\n10. Could not vote,\n11. Would not vote",
"maxValue": 13,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_trust",
"description": "How much trust or distrust do you have in scientists?",
"value": "1. Very high trust,\n2. High trust,\n3. Some trust,\n4. Neither trust nor mistrust,\n5. Some mistrust,\n6. High mistrust,\n7. Very high mistrust,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_lr_scale",
"description": "In politics, you often talk about the 'left' and the 'right'. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale?",
"value": "0. 0 - Left,\n1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7,\n8. 8,\n9. 9,\n10. 10,\n11. 11- Right",
"maxValue": 11,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "post",
"description": "0 == Pre deliberation survey responses, 1 == post delibiration survey",
"value": ". ",
"maxValue": "-Inf",
"minValue": "Inf",
"@type": "propertyValue"
},
{
"name": "fg_sikkert_post",
"description": "Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation?",
"value": "1. Veldig negativ,\n2. Veldig negativ,\n3. Veldig negativ",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fo_delib",
"description": "Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts?",
"value": "1. Veldig negativ,\n2. Veldig negativ,\n3. Veldig negativ,\n4. Veldig negativ",
"maxValue": 4,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fo_media",
"description": "Vi takker så mye for din deltakelse! \r\n\r\nHelt til slutt: Det kan hende noen media ønsker å komme i kontakt med enkelte deltakere for å høre om deres opplevelser med å delta i dette arrangementet. Dersom det skulle komme en slik henvendelse, vil du være villig til at de tar kontakt med deg? \r\n\r\nSvaret ditt er ikke bindende.",
"value": "4. Veldig negativ,\n5. Veldig negativ",
"maxValue": 5,
"minValue": 4,
"@type": "propertyValue"
},
{
"name": "on_sikkert_post",
"description": "Føler du deg mer sikker eller mer usikker i ditt syn på ulike metoder for å fjerne CO2 fra luften etter at du deltok i deliberasjonen?",
"value": "1. Veldig negativ,\n2. Veldig negativ,\n3. Veldig negativ",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg",
"description": "1 == Treatment group (AI), 0 == control group (carbon capture)",
"value": ". ",
"maxValue": "-Inf",
"minValue": "Inf",
"@type": "propertyValue"
}
]
}`
Dataset name: .
The dataset has N=207 rows and 35 columns. 0 rows have no missing values on any column.
|
#Variables
Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event.
## No non-missing values to show.
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|
| sikker_fg | Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event. | haven_labelled | 207 | 0 | Inf | NA | -Inf | 2 | F40.0 | 5 |
| name | value |
|---|---|
| Jeg vil delta i undersøkelsen | 1 |
| Jeg samtykker ikke og vil ikke delta i undersøkelsen eller det påfølgende arrangementet | 2 |
First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence
Distribution of values for fg_kunnskap_1
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap_1 | First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence | haven_labelled | 1 | 0.9951691 | 1 | 4 | 5 | 3.490291 | 0.7634344 | 6 | ▁▂▁▅▁▇▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very good knowledge | 1 |
| Good knowledge | 2 |
| Somwhat good knowledge | 3 |
| Little knowledge | 4 |
| No knowledge at all | 5 |
| Do not know | 6 |
First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway
Distribution of values for fg_kunnskap_2
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap_2 | First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway | haven_labelled | 1 | 0.9951691 | 1 | 4 | 6 | 3.747573 | 0.9649309 | 6 | ▁▂▁▂▇▁▃▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very good knowledge | 1 |
| Good knowledge | 2 |
| Somwhat good knowledge | 3 |
| Little knowledge | 4 |
| No knowledge at all | 5 |
| Do not know | 6 |
First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work
Distribution of values for fg_kunnskap_3
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap_3 | First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work | haven_labelled | 1 | 0.9951691 | 1 | 4 | 6 | 3.771845 | 0.9167192 | 6 | ▁▂▁▃▇▁▃▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very good knowledge | 1 |
| Good knowledge | 2 |
| Somwhat good knowledge | 3 |
| Little knowledge | 4 |
| No knowledge at all | 5 |
| Do not know | 6 |
Based on what you know, do you think the Norwegian authorities’ use of machine learning and artificial intelligence will lead to an improvement or worsening of public services?
Distribution of values for fg_forbedring
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_forbedring | Based on what you know, do you think the Norwegian authorities’ use of machine learning and artificial intelligence will lead to an improvement or worsening of public services? | haven_labelled | 1 | 0.9951691 | 1 | 3 | 9 | 3.339806 | 1.546378 | 8 | ▃▇▁▁▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very strong improvement | 1 |
| Strong improvement | 2 |
| Some improvement | 3 |
| Neither improvement or worsening | 4 |
| Some worsening | 5 |
| Strong worsening | 6 |
| Very strong worsening | 7 |
| Do not know | 9 |
And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it
Distribution of values for fg_likhet_1
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_likhet_1 | And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it | haven_labelled | 1 | 0.9951691 | 1 | 4 | 8 | 4.145631 | 1.847386 | 7 | ▁▃▇▅▅▃▂▂ | F40.0 | 5 |
| name | value |
|---|---|
| Very much more | 1 |
| More | 2 |
| Somwhat more | 3 |
| Neither more or less | 4 |
| Less | 6 |
| Very much less | 7 |
| Do not know | 8 |
And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector
Distribution of values for fg_likhet_2
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_likhet_2 | And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector | haven_labelled | 1 | 0.9951691 | 1 | 3 | 8 | 3.019417 | 1.45153 | 8 | ▂▆▇▂▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very much more | 1 |
| More | 2 |
| Somwhat more | 3 |
| Neither more or less | 4 |
| Somewhat less | 5 |
| Less | 6 |
| Very much less | 7 |
| Do not know | 8 |
And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities’ decisions
Distribution of values for fg_likhet_3
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_likhet_3 | And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities’ decisions | haven_labelled | 1 | 0.9951691 | 2 | 4 | 8 | 4.330097 | 1.60723 | 8 | ▂▅▇▃▁▁▁▂ | F40.0 | 5 |
| name | value |
|---|---|
| Very much more | 1 |
| More | 2 |
| Somwhat more | 3 |
| Neither more or less | 4 |
| Somewhat less | 5 |
| Less | 6 |
| Very much less | 7 |
| Do not know | 8 |
Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees?
Distribution of values for fg_kunnskap2
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap2 | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees? | haven_labelled | 1 | 0.9951691 | 1 | 5 | 8 | 4.873786 | 1.307984 | 8 | ▁▁▂▂▇▅▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Oppose very strongly | 1 |
| Oppose strongly | 2 |
| Oppose somewhat | 3 |
| Neither oppose or support | 4 |
| Support somewhat | 5 |
| Support strongly | 6 |
| Support very stringly | 7 |
| Do not know | 8 |
Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer?
Distribution of values for fg_auto_flykt
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_auto_flykt | Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1 | 0.9951691 | 1 | 1 | 4 | 1.160194 | 0.5022563 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 4 |
Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities?
Distribution of values for fg_region
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_region | Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities? | haven_labelled | 1 | 0.9951691 | 1 | 5 | 8 | 4.699029 | 1.563829 | 8 | ▁▁▅▇▇▆▁▂ | F40.0 | 5 |
| name | value |
|---|---|
| Oppose very strongly | 1 |
| Oppose strongly | 2 |
| Oppose somewhat | 3 |
| Neither oppose or support | 4 |
| Support somewhat | 5 |
| Support strongly | 6 |
| Support very stringly | 7 |
| Do not know | 8 |
Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison?
Distribution of values for fg_kjennskap_prv
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kjennskap_prv | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison? | haven_labelled | 1 | 0.9951691 | 1 | 5 | 8 | 4.116505 | 1.645695 | 8 | ▂▃▅▂▇▃▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Oppose very strongly | 1 |
| Oppose strongly | 2 |
| Oppose somewhat | 3 |
| Neither oppose or support | 4 |
| Support somewhat | 5 |
| Support strongly | 6 |
| Support very stringly | 7 |
| Do not know | 8 |
Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer?
Distribution of values for fg_auto_prv
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_auto_prv | Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1 | 0.9951691 | 1 | 1 | 3 | 1.097087 | 0.3829173 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 3 |
How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees
Distribution of values for fg_imp_1
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_imp_1 | How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees | haven_labelled | 1 | 0.9951691 | 1 | 4 | 6 | 3.927185 | 0.7774498 | 6 | ▁▁▁▃▇▁▃▁ | F40.0 | 5 |
| name | value |
|---|---|
| Not important at all | 1 |
| Not important | 2 |
| Somwhat important | 3 |
| Very important | 4 |
| Extremely improtant | 5 |
| Do not know | 6 |
How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting
Distribution of values for fg_imp_2
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_imp_2 | How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting | haven_labelled | 1 | 0.9951691 | 2 | 4 | 6 | 3.864078 | 0.7463543 | 6 | ▁▅▁▇▁▂▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Not important at all | 1 |
| Not important | 2 |
| Somwhat important | 3 |
| Very important | 4 |
| Extremely improtant | 5 |
| Do not know | 6 |
How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates
Distribution of values for fg_imp_3
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_imp_3 | How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates | haven_labelled | 1 | 0.9951691 | 1 | 5 | 6 | 4.446602 | 0.6946226 | 6 | ▁▁▁▁▆▁▇▁ | F40.0 | 5 |
| name | value |
|---|---|
| Not important at all | 1 |
| Not important | 2 |
| Somwhat important | 3 |
| Very important | 4 |
| Extremely improtant | 5 |
| Do not know | 6 |
Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen?
Distribution of values for fg_flykt_post
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_flykt_post | Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen? | haven_labelled | 1 | 0.9951691 | 1 | 2 | 3 | 1.718447 | 0.5745408 | 3 | ▅▁▁▇▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 3 |
Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen?
Distribution of values for fg_prv_post
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_prv_post | Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen? | haven_labelled | 1 | 0.9951691 | 1 | 2 | 3 | 1.597087 | 0.646032 | 3 | ▇▁▁▇▁▁▁▂ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 3 |
Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer?
Distribution of values for fg_tradeoff_1
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_tradeoff_1 | Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer? | haven_labelled | 1 | 0.9951691 | 1 | 2 | 3 | 1.737864 | 0.8080228 | 3 | ▇▁▁▅▁▁▁▃ | F40.0 | 5 |
| name | value |
|---|---|
| A | 1 |
| B | 2 |
| Do not know | 3 |
Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two
Distribution of values for fg_tradeoff_2
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_tradeoff_2 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two | haven_labelled | 1 | 0.9951691 | 1 | 2 | 3 | 1.956311 | 0.8571274 | 3 | ▇▁▁▆▁▁▁▇ | F40.0 | 5 |
| name | value |
|---|---|
| X | 1 |
| Y | 2 |
| Do not know | 3 |
Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive
Distribution of values for fg_tradeoff_3
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_tradeoff_3 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive | haven_labelled | 1 | 0.9951691 | 1 | 2 | 3 | 1.975728 | 0.8111378 | 3 | ▇▁▁▇▁▁▁▇ | F40.0 | 5 |
| name | value |
|---|---|
| One | 1 |
| Two | 2 |
| Do not know | 3 |
What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you.
## No non-missing values to show.
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_edu | What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you. | haven_labelled | 207 | 0 | Inf | NA | -Inf | 7 | F40.0 | 5 |
| name | value |
|---|---|
| No eductation | 1 |
| Pre-school | 2 |
| High school | 3 |
| Vocational school level (includes educations that are based on upper secondary school, but which are not approved as university and college education) | 5 |
| University or collage, up to 4 yearrs | 6 |
| University or collage, more than 4 yearrs | 7 |
| Other | 9 |
Which party would you vote for if there were a parliamentary election tomorrow?
## No non-missing values to show.
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_party | Which party would you vote for if there were a parliamentary election tomorrow? | haven_labelled | 207 | 0 | Inf | NA | -Inf | 12 | F40.0 | 5 |
| name | value |
|---|---|
| Progress party | 1 |
| Conservative party | 2 |
| Liberal Party | 3 |
| Christian democrats | 4 |
| Green party | 13 |
| Center party | 5 |
| Labour party | 6 |
| Socialist party | 7 |
| Red party | 8 |
| Other | 9 |
| Could not vote | 10 |
| Would not vote | 11 |
How much trust or distrust do you have in scientists?
## No non-missing values to show.
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_trust | How much trust or distrust do you have in scientists? | haven_labelled | 207 | 0 | Inf | NA | -Inf | 8 | F40.0 | 5 |
| name | value |
|---|---|
| Very high trust | 1 |
| High trust | 2 |
| Some trust | 3 |
| Neither trust nor mistrust | 4 |
| Some mistrust | 5 |
| High mistrust | 6 |
| Very high mistrust | 7 |
| Do not know | 8 |
In politics, you often talk about the ‘left’ and the ‘right’. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale?
## No non-missing values to show.
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_lr_scale | In politics, you often talk about the ‘left’ and the ‘right’. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale? | haven_labelled | 207 | 0 | Inf | NA | -Inf | 12 | F40.0 | 5 |
| name | value |
|---|---|
| 0 - Left | 0 |
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
| 4 | 4 |
| 5 | 5 |
| 6 | 6 |
| 7 | 7 |
| 8 | 8 |
| 9 | 9 |
| 10 | 10 |
| 11- Right | 11 |
0 == Pre deliberation survey responses, 1 == post delibiration survey
Distribution of values for post
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| post | 0 == Pre deliberation survey responses, 1 == post delibiration survey | numeric | 0 | 1 | 1 | 1 | 1 | 1 | 0 | ▁▁▇▁▁ |
Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation?
Distribution of values for fg_sikkert_post
90 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_sikkert_post | Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation? | haven_labelled | 90 | 0.5652174 | 1 | 3 | 3 | 2.666667 | 0.5085476 | 3 | ▁▁▁▃▁▁▁▇ | F40.0 | 5 |
| name | value |
|---|---|
| Veldig negativ | 1 |
| Veldig negativ | 2 |
| Veldig negativ | 3 |
Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts?
Distribution of values for fo_delib
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fo_delib | Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts? | haven_labelled | 1 | 0.9951691 | 1 | 2 | 3 | 1.626214 | 0.6097518 | 4 | ▇▁▁▇▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Veldig negativ | 1 |
| Veldig negativ | 2 |
| Veldig negativ | 3 |
| Veldig negativ | 4 |
Vi takker så mye for din deltakelse!
Helt til slutt: Det kan hende noen media ønsker å komme i kontakt med enkelte deltakere for å høre om deres opplevelser med å delta i dette arrangementet. Dersom det skulle komme en slik henvendelse, vil du være villig til at de tar kontakt med deg?
Svaret ditt er ikke bindende.
Distribution of values for fo_media
2 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fo_media | Vi takker så mye for din deltakelse! |
Helt til slutt: Det kan hende noen media ønsker å komme i
kontakt med enkelte deltakere for å høre om deres opplevelser med å
delta i dette arrangementet. Dersom det skulle komme en slik
henvendelse, vil du være villig til at de tar kontakt med deg?
Svaret ditt er ikke bindende. |haven_labelled | 2| 0.9903382|4 |4 |5
| 4.346342| 0.4769683| 2|▇▁▁▁▁▁▁▅ |F40.0 |5 |
| name | value |
|---|---|
| Veldig negativ | 4 |
| Veldig negativ | 5 |
Føler du deg mer sikker eller mer usikker i ditt syn på ulike metoder for å fjerne CO2 fra luften etter at du deltok i deliberasjonen?
Distribution of values for on_sikkert_post
118 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| on_sikkert_post | Føler du deg mer sikker eller mer usikker i ditt syn på ulike metoder for å fjerne CO2 fra luften etter at du deltok i deliberasjonen? | haven_labelled | 118 | 0.4299517 | 1 | 3 | 3 | 2.52809 | 0.6757132 | 3 | ▁▁▁▃▁▁▁▇ | F40.0 | 5 |
| name | value |
|---|---|
| Veldig negativ | 1 |
| Veldig negativ | 2 |
| Veldig negativ | 3 |
1 == Treatment group (AI), 0 == control group (carbon capture)
Distribution of values for fg
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| fg | 1 == Treatment group (AI), 0 == control group (carbon capture) | numeric | 0 | 1 | 0 | 1 | 1 | 0.5700483 | 0.4962691 | ▆▁▁▁▇ |
| name | label | data_type | value_labels | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sikker_fg | Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event. | haven_labelled | 1. Jeg vil delta i undersøkelsen, 2. Jeg samtykker ikke og vil ikke delta i undersøkelsen eller det påfølgende arrangementet |
207 | 0.0000000 | Inf | NA | -Inf | NaN | NA | 2 | F40.0 | 5 | |
| fg_kunnskap_1 | First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence | haven_labelled | 1. Very good knowledge, 2. Good knowledge, 3. Somwhat good knowledge, 4. Little knowledge, 5. No knowledge at all, 6. Do not know |
1 | 0.9951691 | 1 | 4 | 5 | 3.4902913 | 0.7634344 | 6 | ▁▂▁▅▁▇▁▁ | F40.0 | 5 |
| fg_kunnskap_2 | First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway | haven_labelled | 1. Very good knowledge, 2. Good knowledge, 3. Somwhat good knowledge, 4. Little knowledge, 5. No knowledge at all, 6. Do not know |
1 | 0.9951691 | 1 | 4 | 6 | 3.7475728 | 0.9649309 | 6 | ▁▂▁▂▇▁▃▁ | F40.0 | 5 |
| fg_kunnskap_3 | First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work | haven_labelled | 1. Very good knowledge, 2. Good knowledge, 3. Somwhat good knowledge, 4. Little knowledge, 5. No knowledge at all, 6. Do not know |
1 | 0.9951691 | 1 | 4 | 6 | 3.7718447 | 0.9167192 | 6 | ▁▂▁▃▇▁▃▁ | F40.0 | 5 |
| fg_forbedring | Based on what you know, do you think the Norwegian authorities’ use of machine learning and artificial intelligence will lead to an improvement or worsening of public services? | haven_labelled | 1. Very strong improvement, 2. Strong improvement, 3. Some improvement, 4. Neither improvement or worsening, 5. Some worsening, 6. Strong worsening, 7. Very strong worsening, 9. Do not know |
1 | 0.9951691 | 1 | 3 | 9 | 3.3398058 | 1.5463783 | 8 | ▃▇▁▁▁▁▁▁ | F40.0 | 5 |
| fg_likhet_1 | And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it | haven_labelled | 1. Very much more, 2. More, 3. Somwhat more, 4. Neither more or less, 6. Less, 7. Very much less, 8. Do not know |
1 | 0.9951691 | 1 | 4 | 8 | 4.1456311 | 1.8473858 | 7 | ▁▃▇▅▅▃▂▂ | F40.0 | 5 |
| fg_likhet_2 | And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector | haven_labelled | 1. Very much more, 2. More, 3. Somwhat more, 4. Neither more or less, 5. Somewhat less, 6. Less, 7. Very much less, 8. Do not know |
1 | 0.9951691 | 1 | 3 | 8 | 3.0194175 | 1.4515296 | 8 | ▂▆▇▂▁▁▁▁ | F40.0 | 5 |
| fg_likhet_3 | And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities’ decisions | haven_labelled | 1. Very much more, 2. More, 3. Somwhat more, 4. Neither more or less, 5. Somewhat less, 6. Less, 7. Very much less, 8. Do not know |
1 | 0.9951691 | 2 | 4 | 8 | 4.3300971 | 1.6072297 | 8 | ▂▅▇▃▁▁▁▂ | F40.0 | 5 |
| fg_kunnskap2 | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
1 | 0.9951691 | 1 | 5 | 8 | 4.8737864 | 1.3079838 | 8 | ▁▁▂▂▇▅▁▁ | F40.0 | 5 |
| fg_auto_flykt | Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 4. Do not know |
1 | 0.9951691 | 1 | 1 | 4 | 1.1601942 | 0.5022563 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| fg_region | Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
1 | 0.9951691 | 1 | 5 | 8 | 4.6990291 | 1.5638286 | 8 | ▁▁▅▇▇▆▁▂ | F40.0 | 5 |
| fg_kjennskap_nav | Based on your knowledge, to what extent do you support that NAV should be open to using artificial intelligence when deciding who should be invited to a dialogue meeting? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
1 | 0.9951691 | 1 | 5 | 8 | 4.8349515 | 1.4590146 | 8 | ▁▁▂▁▇▅▁▁ | F40.0 | 5 |
| fg_auto_nav | Imagine two situations. In one situation, a case manager will invite to a dialogue meeting based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
1 | 0.9951691 | 1 | 1 | 3 | 1.1456311 | 0.4050358 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| fg_pari_nav | Machine learning involves getting computers to learn to solve tasks based on data material. A dilemma that often arises is that different algorithms have different advantages and disadvantages, and you have to choose which considerations to prioritize. Imagine that NAV has to choose between using one of these two algorithms: The first algorithm is most accurate. Among those who need a dialogue meeting, however, more [Field-fairgov_pari_treat] are offered a dialogue meeting, even though there are an equal number of women and men who are on sick leave and need a dialogue meeting. The proportion who need a dialogue meeting without receiving an offer is therefore larger at [Field-fairgov_pari_treat_2]. The second algorithm ensures that the proportion of those on sick leave who are called to a dialogue meeting is equal for women and men. However, it is less accurate overall, so that fewer people who need a dialogue meeting are called. This applies to both women and men. If it was only between these two, which one do you think seems more fair? | haven_labelled | 1. The first algorithm seems most fair, 2. The second algorithm seems most fair, 3. Do not know |
1 | 0.9951691 | 1 | 1 | 3 | 1.5873786 | 0.7188310 | 3 | ▇▁▁▅▁▁▁▂ | F40.0 | 5 |
| fg_kjennskap_prv | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
1 | 0.9951691 | 1 | 5 | 8 | 4.1165049 | 1.6456949 | 8 | ▂▃▅▂▇▃▁▁ | F40.0 | 5 |
| fg_auto_prv | Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
1 | 0.9951691 | 1 | 1 | 3 | 1.0970874 | 0.3829173 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| fg_imp_1 | How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees | haven_labelled | 1. Not important at all, 2. Not important, 3. Somwhat important, 4. Very important, 5. Extremely improtant, 6. Do not know |
1 | 0.9951691 | 1 | 4 | 6 | 3.9271845 | 0.7774498 | 6 | ▁▁▁▃▇▁▃▁ | F40.0 | 5 |
| fg_imp_2 | How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting | haven_labelled | 1. Not important at all, 2. Not important, 3. Somwhat important, 4. Very important, 5. Extremely improtant, 6. Do not know |
1 | 0.9951691 | 2 | 4 | 6 | 3.8640777 | 0.7463543 | 6 | ▁▅▁▇▁▂▁▁ | F40.0 | 5 |
| fg_imp_3 | How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates | haven_labelled | 1. Not important at all, 2. Not important, 3. Somwhat important, 4. Very important, 5. Extremely improtant, 6. Do not know |
1 | 0.9951691 | 1 | 5 | 6 | 4.4466019 | 0.6946226 | 6 | ▁▁▁▁▆▁▇▁ | F40.0 | 5 |
| fg_flykt_post | Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
1 | 0.9951691 | 1 | 2 | 3 | 1.7184466 | 0.5745408 | 3 | ▅▁▁▇▁▁▁▁ | F40.0 | 5 |
| fg_nav_post | Case 2 of 3: Dialogue meeting with NAV As sick leave, you receive follow-up from NAV. They will assess whether you should be invited to a dialogue meeting to discuss what can be done to enable you to return to work. Dialogue meetings require resources, so not everyone can get that offer. When NAV assesses your need, you get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_di_treat] will take much more time than the algorithm, which extends the period accordingly before you possibly receive an invitation to a dialogue meeting. Which decision-making process would you have chosen? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
1 | 0.9951691 | 1 | 2 | 3 | 1.6504854 | 0.5878234 | 3 | ▆▁▁▇▁▁▁▁ | F40.0 | 5 |
| fg_prv_post | Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
1 | 0.9951691 | 1 | 2 | 3 | 1.5970874 | 0.6460320 | 3 | ▇▁▁▇▁▁▁▂ | F40.0 | 5 |
| fg_tradeoff_1 | Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer? | haven_labelled | 1. A, 2. B, 3. Do not know |
1 | 0.9951691 | 1 | 2 | 3 | 1.7378641 | 0.8080228 | 3 | ▇▁▁▅▁▁▁▃ | F40.0 | 5 |
| fg_tradeoff_2 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two | haven_labelled | 1. X, 2. Y, 3. Do not know |
1 | 0.9951691 | 1 | 2 | 3 | 1.9563107 | 0.8571274 | 3 | ▇▁▁▆▁▁▁▇ | F40.0 | 5 |
| fg_tradeoff_3 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive | haven_labelled | 1. One, 2. Two, 3. Do not know |
1 | 0.9951691 | 1 | 2 | 3 | 1.9757282 | 0.8111378 | 3 | ▇▁▁▇▁▁▁▇ | F40.0 | 5 |
| demo_edu | What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you. | haven_labelled | 1. No eductation, 2. Pre-school, 3. High school, 5. Vocational school level (includes educations that are based on upper secondary school, but which are not approved as university and college education), 6. University or collage, up to 4 yearrs, 7. University or collage, more than 4 yearrs, 9. Other |
207 | 0.0000000 | Inf | NA | -Inf | NaN | NA | 7 | F40.0 | 5 | |
| demo_party | Which party would you vote for if there were a parliamentary election tomorrow? | haven_labelled | 1. Progress party, 2. Conservative party, 3. Liberal Party, 4. Christian democrats, 13. Green party, 5. Center party, 6. Labour party, 7. Socialist party, 8. Red party, 9. Other, 10. Could not vote, 11. Would not vote |
207 | 0.0000000 | Inf | NA | -Inf | NaN | NA | 12 | F40.0 | 5 | |
| demo_trust | How much trust or distrust do you have in scientists? | haven_labelled | 1. Very high trust, 2. High trust, 3. Some trust, 4. Neither trust nor mistrust, 5. Some mistrust, 6. High mistrust, 7. Very high mistrust, 8. Do not know |
207 | 0.0000000 | Inf | NA | -Inf | NaN | NA | 8 | F40.0 | 5 | |
| demo_lr_scale | In politics, you often talk about the ‘left’ and the ‘right’. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale? | haven_labelled | 0. 0 - Left, 1. 1, 2. 2, 3. 3, 4. 4, 5. 5, 6. 6, 7. 7, 8. 8, 9. 9, 10. 10, 11. 11- Right |
207 | 0.0000000 | Inf | NA | -Inf | NaN | NA | 12 | F40.0 | 5 | |
| post | 0 == Pre deliberation survey responses, 1 == post delibiration survey | numeric | . | 0 | 1.0000000 | 1 | 1 | 1 | 1.0000000 | 0.0000000 | NA | ▁▁▇▁▁ | NA | NA |
| fg_sikkert_post | Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation? | haven_labelled | 1. Veldig negativ, 2. Veldig negativ, 3. Veldig negativ |
90 | 0.5652174 | 1 | 3 | 3 | 2.6666667 | 0.5085476 | 3 | ▁▁▁▃▁▁▁▇ | F40.0 | 5 |
| fo_delib | Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts? | haven_labelled | 1. Veldig negativ, 2. Veldig negativ, 3. Veldig negativ, 4. Veldig negativ |
1 | 0.9951691 | 1 | 2 | 3 | 1.6262136 | 0.6097518 | 4 | ▇▁▁▇▁▁▁▁ | F40.0 | 5 |
| fo_media | Vi takker så mye for din deltakelse! |
Helt til slutt: Det kan hende noen media ønsker å komme i
kontakt med enkelte deltakere for å høre om deres opplevelser med å
delta i dette arrangementet. Dersom det skulle komme en slik
henvendelse, vil du være villig til at de tar kontakt med deg?
Svaret ditt er ikke bindende. |haven_labelled |4. Veldig
negativ,
5. Veldig negativ | 2| 0.9903382|4 |4 |5 | 4.3463415|
0.4769683| 2|▇▁▁▁▁▁▁▅ |F40.0 |5 |
|on_sikkert_post |Føler du deg mer sikker
eller mer usikker i ditt syn på ulike metoder for å fjerne CO2 fra
luften etter at du deltok i deliberasjonen? |haven_labelled |1. Veldig
negativ,
2. Veldig negativ,
3. Veldig negativ | 118| 0.4299517|1
|3 |3 | 2.5280899| 0.6757132| 3|▁▁▁▃▁▁▁▇ |F40.0 |5 |
|fg |1 == Treatment group (AI), 0 == control group
(carbon capture) |numeric |. | 0| 1.0000000|0 |1 |1 | 0.5700483|
0.4962691| NA|▆▁▁▁▇ |NA |NA |
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": ".",
"datePublished": "2022-11-16",
"description": "The dataset has N=207 rows and 35 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
"keywords": ["sikker_fg", "fg_kunnskap_1", "fg_kunnskap_2", "fg_kunnskap_3", "fg_forbedring", "fg_likhet_1", "fg_likhet_2", "fg_likhet_3", "fg_kunnskap2", "fg_auto_flykt", "fg_region", "fg_kjennskap_nav", "fg_auto_nav", "fg_pari_nav", "fg_kjennskap_prv", "fg_auto_prv", "fg_imp_1", "fg_imp_2", "fg_imp_3", "fg_flykt_post", "fg_nav_post", "fg_prv_post", "fg_tradeoff_1", "fg_tradeoff_2", "fg_tradeoff_3", "demo_edu", "demo_party", "demo_trust", "demo_lr_scale", "post", "fg_sikkert_post", "fo_delib", "fo_media", "on_sikkert_post", "fg"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "sikker_fg",
"description": "Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event.",
"value": "1. Jeg vil delta i undersøkelsen,\n2. Jeg samtykker ikke og vil ikke delta i undersøkelsen eller det påfølgende arrangementet",
"maxValue": 2,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap_1",
"description": "First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence",
"value": "1. Very good knowledge,\n2. Good knowledge,\n3. Somwhat good knowledge,\n4. Little knowledge,\n5. No knowledge at all,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap_2",
"description": "First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway",
"value": "1. Very good knowledge,\n2. Good knowledge,\n3. Somwhat good knowledge,\n4. Little knowledge,\n5. No knowledge at all,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap_3",
"description": "First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work",
"value": "1. Very good knowledge,\n2. Good knowledge,\n3. Somwhat good knowledge,\n4. Little knowledge,\n5. No knowledge at all,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_forbedring",
"description": "Based on what you know, do you think the Norwegian authorities' use of machine learning and artificial intelligence will lead to an improvement or worsening of public services?",
"value": "1. Very strong improvement,\n2. Strong improvement,\n3. Some improvement,\n4. Neither improvement or worsening,\n5. Some worsening,\n6. Strong worsening,\n7. Very strong worsening,\n9. Do not know",
"maxValue": 9,
"minValue": 1,
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{
"name": "fg_likhet_1",
"description": "And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it",
"value": "1. Very much more,\n2. More,\n3. Somwhat more,\n4. Neither more or less,\n6. Less,\n7. Very much less,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
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{
"name": "fg_likhet_2",
"description": "And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector",
"value": "1. Very much more,\n2. More,\n3. Somwhat more,\n4. Neither more or less,\n5. Somewhat less,\n6. Less,\n7. Very much less,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
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},
{
"name": "fg_likhet_3",
"description": "And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities' decisions",
"value": "1. Very much more,\n2. More,\n3. Somwhat more,\n4. Neither more or less,\n5. Somewhat less,\n6. Less,\n7. Very much less,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap2",
"description": "Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
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{
"name": "fg_auto_flykt",
"description": "Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n4. Do not know",
"maxValue": 4,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_region",
"description": "Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
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},
{
"name": "fg_kjennskap_nav",
"description": "Based on your knowledge, to what extent do you support that NAV should be open to using artificial intelligence when deciding who should be invited to a dialogue meeting?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
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},
{
"name": "fg_auto_nav",
"description": "Imagine two situations. In one situation, a case manager will invite to a dialogue meeting based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_pari_nav",
"description": "Machine learning involves getting computers to learn to solve tasks based on data material. A dilemma that often arises is that different algorithms have different advantages and disadvantages, and you have to choose which considerations to prioritize. Imagine that NAV has to choose between using one of these two algorithms: The first algorithm is most accurate. Among those who need a dialogue meeting, however, more [Field-fairgov_pari_treat] are offered a dialogue meeting, even though there are an equal number of women and men who are on sick leave and need a dialogue meeting. The proportion who need a dialogue meeting without receiving an offer is therefore larger at [Field-fairgov_pari_treat_2]. The second algorithm ensures that the proportion of those on sick leave who are called to a dialogue meeting is equal for women and men. However, it is less accurate overall, so that fewer people who need a dialogue meeting are called. This applies to both women and men. If it was only between these two, which one do you think seems more fair?",
"value": "1. The first algorithm seems most fair,\n2. The second algorithm seems most fair,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kjennskap_prv",
"description": "Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_auto_prv",
"description": "Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_imp_1",
"description": "How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees",
"value": "1. Not important at all,\n2. Not important,\n3. Somwhat important,\n4. Very important,\n5. Extremely improtant,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_imp_2",
"description": "How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting",
"value": "1. Not important at all,\n2. Not important,\n3. Somwhat important,\n4. Very important,\n5. Extremely improtant,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_imp_3",
"description": "How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates",
"value": "1. Not important at all,\n2. Not important,\n3. Somwhat important,\n4. Very important,\n5. Extremely improtant,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_flykt_post",
"description": "Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_nav_post",
"description": "Case 2 of 3: Dialogue meeting with NAV As sick leave, you receive follow-up from NAV. They will assess whether you should be invited to a dialogue meeting to discuss what can be done to enable you to return to work. Dialogue meetings require resources, so not everyone can get that offer. When NAV assesses your need, you get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_di_treat] will take much more time than the algorithm, which extends the period accordingly before you possibly receive an invitation to a dialogue meeting. Which decision-making process would you have chosen?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_prv_post",
"description": "Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_tradeoff_1",
"description": "Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer?",
"value": "1. A,\n2. B,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_tradeoff_2",
"description": "Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two",
"value": "1. X,\n2. Y,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_tradeoff_3",
"description": "Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive",
"value": "1. One,\n2. Two,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_edu",
"description": "What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you.",
"value": "1. No eductation,\n2. Pre-school,\n3. High school,\n5. Vocational school level (includes educations that are based on upper secondary school, but which are not approved as university and college education),\n6. University or collage, up to 4 yearrs,\n7. University or collage, more than 4 yearrs,\n9. Other",
"maxValue": 9,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_party",
"description": "Which party would you vote for if there were a parliamentary election tomorrow?",
"value": "1. Progress party,\n2. Conservative party,\n3. Liberal Party,\n4. Christian democrats,\n13. Green party,\n5. Center party,\n6. Labour party,\n7. Socialist party,\n8. Red party,\n9. Other,\n10. Could not vote,\n11. Would not vote",
"maxValue": 13,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_trust",
"description": "How much trust or distrust do you have in scientists?",
"value": "1. Very high trust,\n2. High trust,\n3. Some trust,\n4. Neither trust nor mistrust,\n5. Some mistrust,\n6. High mistrust,\n7. Very high mistrust,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_lr_scale",
"description": "In politics, you often talk about the 'left' and the 'right'. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale?",
"value": "0. 0 - Left,\n1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7,\n8. 8,\n9. 9,\n10. 10,\n11. 11- Right",
"maxValue": 11,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "post",
"description": "0 == Pre deliberation survey responses, 1 == post delibiration survey",
"value": ". ",
"maxValue": "-Inf",
"minValue": "Inf",
"@type": "propertyValue"
},
{
"name": "fg_sikkert_post",
"description": "Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation?",
"value": "1. Veldig negativ,\n2. Veldig negativ,\n3. Veldig negativ",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fo_delib",
"description": "Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts?",
"value": "1. Veldig negativ,\n2. Veldig negativ,\n3. Veldig negativ,\n4. Veldig negativ",
"maxValue": 4,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fo_media",
"description": "Vi takker så mye for din deltakelse! \r\n\r\nHelt til slutt: Det kan hende noen media ønsker å komme i kontakt med enkelte deltakere for å høre om deres opplevelser med å delta i dette arrangementet. Dersom det skulle komme en slik henvendelse, vil du være villig til at de tar kontakt med deg? \r\n\r\nSvaret ditt er ikke bindende.",
"value": "4. Veldig negativ,\n5. Veldig negativ",
"maxValue": 5,
"minValue": 4,
"@type": "propertyValue"
},
{
"name": "on_sikkert_post",
"description": "Føler du deg mer sikker eller mer usikker i ditt syn på ulike metoder for å fjerne CO2 fra luften etter at du deltok i deliberasjonen?",
"value": "1. Veldig negativ,\n2. Veldig negativ,\n3. Veldig negativ",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg",
"description": "1 == Treatment group (AI), 0 == control group (carbon capture)",
"value": ". ",
"maxValue": "-Inf",
"minValue": "Inf",
"@type": "propertyValue"
}
]
}`
Dataset name: .
The dataset has N=414 rows and 35 columns. 0 rows have no missing values on any column.
|
#Variables
Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event.
## No non-missing values to show.
414 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|
| sikker_fg | Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event. | haven_labelled | 414 | 0 | Inf | NA | -Inf | 2 | F40.0 | 5 |
| name | value |
|---|---|
| Jeg vil delta i undersøkelsen | 1 |
| Jeg samtykker ikke og vil ikke delta i undersøkelsen eller det påfølgende arrangementet | 2 |
First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence
Distribution of values for fg_kunnskap_1
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap_1 | First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence | haven_labelled | 1 | 0.9975845 | 1 | 4 | 6 | 3.615012 | 0.7755157 | 6 | ▁▁▁▃▇▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very good knowledge | 1 |
| Good knowledge | 2 |
| Somwhat good knowledge | 3 |
| Little knowledge | 4 |
| No knowledge at all | 5 |
| Do not know | 6 |
First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway
Distribution of values for fg_kunnskap_2
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap_2 | First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway | haven_labelled | 1 | 0.9975845 | 1 | 4 | 6 | 3.883777 | 0.9507551 | 6 | ▁▂▁▂▇▁▃▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very good knowledge | 1 |
| Good knowledge | 2 |
| Somwhat good knowledge | 3 |
| Little knowledge | 4 |
| No knowledge at all | 5 |
| Do not know | 6 |
First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work
Distribution of values for fg_kunnskap_3
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap_3 | First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work | haven_labelled | 1 | 0.9975845 | 1 | 4 | 6 | 3.866828 | 0.994738 | 6 | ▁▂▁▃▇▁▅▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very good knowledge | 1 |
| Good knowledge | 2 |
| Somwhat good knowledge | 3 |
| Little knowledge | 4 |
| No knowledge at all | 5 |
| Do not know | 6 |
Based on what you know, do you think the Norwegian authorities’ use of machine learning and artificial intelligence will lead to an improvement or worsening of public services?
Distribution of values for fg_forbedring
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_forbedring | Based on what you know, do you think the Norwegian authorities’ use of machine learning and artificial intelligence will lead to an improvement or worsening of public services? | haven_labelled | 1 | 0.9975845 | 1 | 3 | 9 | 3.716707 | 1.959452 | 8 | ▃▇▂▂▁▁▁▂ | F40.0 | 5 |
| name | value |
|---|---|
| Very strong improvement | 1 |
| Strong improvement | 2 |
| Some improvement | 3 |
| Neither improvement or worsening | 4 |
| Some worsening | 5 |
| Strong worsening | 6 |
| Very strong worsening | 7 |
| Do not know | 9 |
And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it
Distribution of values for fg_likhet_1
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_likhet_1 | And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it | haven_labelled | 1 | 0.9975845 | 1 | 4 | 8 | 4.472155 | 1.951431 | 7 | ▁▃▇▅▅▃▂▃ | F40.0 | 5 |
| name | value |
|---|---|
| Very much more | 1 |
| More | 2 |
| Somwhat more | 3 |
| Neither more or less | 4 |
| Less | 6 |
| Very much less | 7 |
| Do not know | 8 |
And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector
Distribution of values for fg_likhet_2
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_likhet_2 | And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector | haven_labelled | 1 | 0.9975845 | 1 | 3 | 8 | 3.336562 | 1.72659 | 8 | ▂▆▇▃▁▁▁▂ | F40.0 | 5 |
| name | value |
|---|---|
| Very much more | 1 |
| More | 2 |
| Somwhat more | 3 |
| Neither more or less | 4 |
| Somewhat less | 5 |
| Less | 6 |
| Very much less | 7 |
| Do not know | 8 |
And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities’ decisions
Distribution of values for fg_likhet_3
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_likhet_3 | And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities’ decisions | haven_labelled | 1 | 0.9975845 | 1 | 4 | 8 | 4.530266 | 1.697279 | 8 | ▁▂▃▇▃▂▁▂ | F40.0 | 5 |
| name | value |
|---|---|
| Very much more | 1 |
| More | 2 |
| Somwhat more | 3 |
| Neither more or less | 4 |
| Somewhat less | 5 |
| Less | 6 |
| Very much less | 7 |
| Do not know | 8 |
Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees?
Distribution of values for fg_kunnskap2
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kunnskap2 | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees? | haven_labelled | 1 | 0.9975845 | 1 | 5 | 8 | 4.619855 | 1.364705 | 8 | ▁▁▃▅▇▃▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Oppose very strongly | 1 |
| Oppose strongly | 2 |
| Oppose somewhat | 3 |
| Neither oppose or support | 4 |
| Support somewhat | 5 |
| Support strongly | 6 |
| Support very stringly | 7 |
| Do not know | 8 |
Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer?
Distribution of values for fg_auto_flykt
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_auto_flykt | Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1 | 0.9975845 | 1 | 1 | 4 | 1.244552 | 0.6578507 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 4 |
Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities?
Distribution of values for fg_region
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_region | Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities? | haven_labelled | 1 | 0.9975845 | 1 | 5 | 8 | 4.62954 | 1.547249 | 8 | ▁▂▃▇▇▆▁▂ | F40.0 | 5 |
| name | value |
|---|---|
| Oppose very strongly | 1 |
| Oppose strongly | 2 |
| Oppose somewhat | 3 |
| Neither oppose or support | 4 |
| Support somewhat | 5 |
| Support strongly | 6 |
| Support very stringly | 7 |
| Do not know | 8 |
Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison?
Distribution of values for fg_kjennskap_prv
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_kjennskap_prv | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison? | haven_labelled | 1 | 0.9975845 | 1 | 4 | 8 | 3.905569 | 1.727362 | 8 | ▂▃▅▃▇▂▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Oppose very strongly | 1 |
| Oppose strongly | 2 |
| Oppose somewhat | 3 |
| Neither oppose or support | 4 |
| Support somewhat | 5 |
| Support strongly | 6 |
| Support very stringly | 7 |
| Do not know | 8 |
Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer?
Distribution of values for fg_auto_prv
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_auto_prv | Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1 | 0.9975845 | 1 | 1 | 3 | 1.108959 | 0.3944298 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 3 |
How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees
Distribution of values for fg_imp_1
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_imp_1 | How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees | haven_labelled | 1 | 0.9975845 | 1 | 4 | 6 | 3.966102 | 0.7719686 | 6 | ▁▁▁▃▇▁▃▁ | F40.0 | 5 |
| name | value |
|---|---|
| Not important at all | 1 |
| Not important | 2 |
| Somwhat important | 3 |
| Very important | 4 |
| Extremely improtant | 5 |
| Do not know | 6 |
How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting
Distribution of values for fg_imp_2
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_imp_2 | How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting | haven_labelled | 1 | 0.9975845 | 1 | 4 | 6 | 3.866828 | 0.7908388 | 6 | ▁▁▁▅▇▁▂▁ | F40.0 | 5 |
| name | value |
|---|---|
| Not important at all | 1 |
| Not important | 2 |
| Somwhat important | 3 |
| Very important | 4 |
| Extremely improtant | 5 |
| Do not know | 6 |
How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates
Distribution of values for fg_imp_3
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_imp_3 | How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates | haven_labelled | 1 | 0.9975845 | 1 | 5 | 6 | 4.440678 | 0.7237252 | 6 | ▁▁▁▁▆▁▇▁ | F40.0 | 5 |
| name | value |
|---|---|
| Not important at all | 1 |
| Not important | 2 |
| Somwhat important | 3 |
| Very important | 4 |
| Extremely improtant | 5 |
| Do not know | 6 |
Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen?
Distribution of values for fg_flykt_post
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_flykt_post | Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen? | haven_labelled | 1 | 0.9975845 | 1 | 2 | 3 | 1.719128 | 0.5561197 | 3 | ▅▁▁▇▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 3 |
Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen?
Distribution of values for fg_prv_post
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_prv_post | Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen? | haven_labelled | 1 | 0.9975845 | 1 | 2 | 3 | 1.624697 | 0.6661513 | 3 | ▇▁▁▇▁▁▁▂ | F40.0 | 5 |
| name | value |
|---|---|
| Manual processing | 1 |
| Algorithmic processing | 2 |
| Do not know | 3 |
Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer?
Distribution of values for fg_tradeoff_1
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_tradeoff_1 | Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer? | haven_labelled | 1 | 0.9975845 | 1 | 1 | 3 | 1.697337 | 0.7961788 | 3 | ▇▁▁▅▁▁▁▃ | F40.0 | 5 |
| name | value |
|---|---|
| A | 1 |
| B | 2 |
| Do not know | 3 |
Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two
Distribution of values for fg_tradeoff_2
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_tradeoff_2 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two | haven_labelled | 1 | 0.9975845 | 1 | 2 | 3 | 1.966102 | 0.8583194 | 3 | ▇▁▁▆▁▁▁▇ | F40.0 | 5 |
| name | value |
|---|---|
| X | 1 |
| Y | 2 |
| Do not know | 3 |
Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive
Distribution of values for fg_tradeoff_3
2 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_tradeoff_3 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive | haven_labelled | 2 | 0.9951691 | 1 | 2 | 3 | 1.980582 | 0.7981802 | 3 | ▇▁▁▇▁▁▁▇ | F40.0 | 5 |
| name | value |
|---|---|
| One | 1 |
| Two | 2 |
| Do not know | 3 |
What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you.
Distribution of values for demo_edu
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_edu | What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you. | haven_labelled | 207 | 0.5 | 2 | 6 | 9 | 5.560386 | 1.571894 | 7 | ▁▅▁▃▇▇▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| No eductation | 1 |
| Pre-school | 2 |
| High school | 3 |
| Vocational school level (includes educations that are based on upper secondary school, but which are not approved as university and college education) | 5 |
| University or collage, up to 4 yearrs | 6 |
| University or collage, more than 4 yearrs | 7 |
| Other | 9 |
Which party would you vote for if there were a parliamentary election tomorrow?
Distribution of values for demo_party
208 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_party | Which party would you vote for if there were a parliamentary election tomorrow? | haven_labelled | 208 | 0.4975845 | 1 | 6 | 13 | 6.145631 | 3.683603 | 12 | ▇▃▁▇▃▂▂▃ | F40.0 | 5 |
| name | value |
|---|---|
| Progress party | 1 |
| Conservative party | 2 |
| Liberal Party | 3 |
| Christian democrats | 4 |
| Green party | 13 |
| Center party | 5 |
| Labour party | 6 |
| Socialist party | 7 |
| Red party | 8 |
| Other | 9 |
| Could not vote | 10 |
| Would not vote | 11 |
How much trust or distrust do you have in scientists?
Distribution of values for demo_trust
207 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_trust | How much trust or distrust do you have in scientists? | haven_labelled | 207 | 0.5 | 1 | 2 | 7 | 2.386473 | 1.072793 | 8 | ▂▇▂▂▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Very high trust | 1 |
| High trust | 2 |
| Some trust | 3 |
| Neither trust nor mistrust | 4 |
| Some mistrust | 5 |
| High mistrust | 6 |
| Very high mistrust | 7 |
| Do not know | 8 |
In politics, you often talk about the ‘left’ and the ‘right’. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale?
Distribution of values for demo_lr_scale
208 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| demo_lr_scale | In politics, you often talk about the ‘left’ and the ‘right’. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale? | haven_labelled | 208 | 0.4975845 | 0 | 5 | 11 | 5.165049 | 2.79755 | 12 | ▂▃▇▆▃▆▁▃ | F40.0 | 5 |
| name | value |
|---|---|
| 0 - Left | 0 |
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
| 4 | 4 |
| 5 | 5 |
| 6 | 6 |
| 7 | 7 |
| 8 | 8 |
| 9 | 9 |
| 10 | 10 |
| 11- Right | 11 |
0 == Pre deliberation survey responses, 1 == post delibiration survey
Distribution of values for post
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| post | 0 == Pre deliberation survey responses, 1 == post delibiration survey | numeric | 0 | 1 | 0 | 0.5 | 1 | 0.5 | 0.500605 | ▇▁▁▁▇ |
Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation?
Distribution of values for fg_sikkert_post
297 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fg_sikkert_post | Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation? | haven_labelled | 297 | 0.2826087 | 1 | 3 | 3 | 2.666667 | 0.5085476 | 3 | ▁▁▁▃▁▁▁▇ | F40.0 | 5 |
| name | value |
|---|---|
| Veldig negativ | 1 |
| Veldig negativ | 2 |
| Veldig negativ | 3 |
Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts?
Distribution of values for fo_delib
208 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fo_delib | Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts? | haven_labelled | 208 | 0.4975845 | 1 | 2 | 3 | 1.626214 | 0.6097518 | 4 | ▇▁▁▇▁▁▁▁ | F40.0 | 5 |
| name | value |
|---|---|
| Veldig negativ | 1 |
| Veldig negativ | 2 |
| Veldig negativ | 3 |
| Veldig negativ | 4 |
Vi takker så mye for din deltakelse!
Helt til slutt: Det kan hende noen media ønsker å komme i kontakt med enkelte deltakere for å høre om deres opplevelser med å delta i dette arrangementet. Dersom det skulle komme en slik henvendelse, vil du være villig til at de tar kontakt med deg?
Svaret ditt er ikke bindende.
Distribution of values for fo_media
209 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fo_media | Vi takker så mye for din deltakelse! |
Helt til slutt: Det kan hende noen media ønsker å komme i
kontakt med enkelte deltakere for å høre om deres opplevelser med å
delta i dette arrangementet. Dersom det skulle komme en slik
henvendelse, vil du være villig til at de tar kontakt med deg?
Svaret ditt er ikke bindende. |haven_labelled | 209| 0.4951691|4 |4
|5 | 4.346342| 0.4769683| 2|▇▁▁▁▁▁▁▅ |F40.0 |5 |
| name | value |
|---|---|
| Veldig negativ | 4 |
| Veldig negativ | 5 |
Føler du deg mer sikker eller mer usikker i ditt syn på ulike metoder for å fjerne CO2 fra luften etter at du deltok i deliberasjonen?
Distribution of values for on_sikkert_post
325 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| on_sikkert_post | Føler du deg mer sikker eller mer usikker i ditt syn på ulike metoder for å fjerne CO2 fra luften etter at du deltok i deliberasjonen? | haven_labelled | 325 | 0.2149758 | 1 | 3 | 3 | 2.52809 | 0.6757132 | 3 | ▁▁▁▃▁▁▁▇ | F40.0 | 5 |
| name | value |
|---|---|
| Veldig negativ | 1 |
| Veldig negativ | 2 |
| Veldig negativ | 3 |
1 == Treatment group (AI), 0 == control group (carbon capture)
Distribution of values for fg
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| fg | 1 == Treatment group (AI), 0 == control group (carbon capture) | numeric | 0 | 1 | 0 | 1 | 1 | 0.5700483 | 0.4956679 | ▆▁▁▁▇ |
| name | label | data_type | value_labels | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sikker_fg | Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event. | haven_labelled | 1. Jeg vil delta i undersøkelsen, 2. Jeg samtykker ikke og vil ikke delta i undersøkelsen eller det påfølgende arrangementet |
414 | 0.0000000 | Inf | NA | -Inf | NaN | NA | 2 | F40.0 | 5 | |
| fg_kunnskap_1 | First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence | haven_labelled | 1. Very good knowledge, 2. Good knowledge, 3. Somwhat good knowledge, 4. Little knowledge, 5. No knowledge at all, 6. Do not know |
1 | 0.9975845 | 1 | 4 | 6 | 3.6150121 | 0.7755157 | 6 | ▁▁▁▃▇▁▁▁ | F40.0 | 5 |
| fg_kunnskap_2 | First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway | haven_labelled | 1. Very good knowledge, 2. Good knowledge, 3. Somwhat good knowledge, 4. Little knowledge, 5. No knowledge at all, 6. Do not know |
1 | 0.9975845 | 1 | 4 | 6 | 3.8837772 | 0.9507551 | 6 | ▁▂▁▂▇▁▃▁ | F40.0 | 5 |
| fg_kunnskap_3 | First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work | haven_labelled | 1. Very good knowledge, 2. Good knowledge, 3. Somwhat good knowledge, 4. Little knowledge, 5. No knowledge at all, 6. Do not know |
1 | 0.9975845 | 1 | 4 | 6 | 3.8668281 | 0.9947380 | 6 | ▁▂▁▃▇▁▅▁ | F40.0 | 5 |
| fg_forbedring | Based on what you know, do you think the Norwegian authorities’ use of machine learning and artificial intelligence will lead to an improvement or worsening of public services? | haven_labelled | 1. Very strong improvement, 2. Strong improvement, 3. Some improvement, 4. Neither improvement or worsening, 5. Some worsening, 6. Strong worsening, 7. Very strong worsening, 9. Do not know |
1 | 0.9975845 | 1 | 3 | 9 | 3.7167070 | 1.9594523 | 8 | ▃▇▂▂▁▁▁▂ | F40.0 | 5 |
| fg_likhet_1 | And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it | haven_labelled | 1. Very much more, 2. More, 3. Somwhat more, 4. Neither more or less, 6. Less, 7. Very much less, 8. Do not know |
1 | 0.9975845 | 1 | 4 | 8 | 4.4721550 | 1.9514308 | 7 | ▁▃▇▅▅▃▂▃ | F40.0 | 5 |
| fg_likhet_2 | And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector | haven_labelled | 1. Very much more, 2. More, 3. Somwhat more, 4. Neither more or less, 5. Somewhat less, 6. Less, 7. Very much less, 8. Do not know |
1 | 0.9975845 | 1 | 3 | 8 | 3.3365617 | 1.7265895 | 8 | ▂▆▇▃▁▁▁▂ | F40.0 | 5 |
| fg_likhet_3 | And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities’ decisions | haven_labelled | 1. Very much more, 2. More, 3. Somwhat more, 4. Neither more or less, 5. Somewhat less, 6. Less, 7. Very much less, 8. Do not know |
1 | 0.9975845 | 1 | 4 | 8 | 4.5302663 | 1.6972791 | 8 | ▁▂▃▇▃▂▁▂ | F40.0 | 5 |
| fg_kunnskap2 | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
1 | 0.9975845 | 1 | 5 | 8 | 4.6198547 | 1.3647052 | 8 | ▁▁▃▅▇▃▁▁ | F40.0 | 5 |
| fg_auto_flykt | Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 4. Do not know |
1 | 0.9975845 | 1 | 1 | 4 | 1.2445521 | 0.6578507 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| fg_region | Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
1 | 0.9975845 | 1 | 5 | 8 | 4.6295400 | 1.5472491 | 8 | ▁▂▃▇▇▆▁▂ | F40.0 | 5 |
| fg_kjennskap_nav | Based on your knowledge, to what extent do you support that NAV should be open to using artificial intelligence when deciding who should be invited to a dialogue meeting? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
1 | 0.9975845 | 1 | 5 | 8 | 4.6077482 | 1.5768772 | 8 | ▁▂▃▂▇▃▁▁ | F40.0 | 5 |
| fg_auto_nav | Imagine two situations. In one situation, a case manager will invite to a dialogue meeting based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
1 | 0.9975845 | 1 | 1 | 3 | 1.1961259 | 0.4702659 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| fg_pari_nav | Machine learning involves getting computers to learn to solve tasks based on data material. A dilemma that often arises is that different algorithms have different advantages and disadvantages, and you have to choose which considerations to prioritize. Imagine that NAV has to choose between using one of these two algorithms: The first algorithm is most accurate. Among those who need a dialogue meeting, however, more [Field-fairgov_pari_treat] are offered a dialogue meeting, even though there are an equal number of women and men who are on sick leave and need a dialogue meeting. The proportion who need a dialogue meeting without receiving an offer is therefore larger at [Field-fairgov_pari_treat_2]. The second algorithm ensures that the proportion of those on sick leave who are called to a dialogue meeting is equal for women and men. However, it is less accurate overall, so that fewer people who need a dialogue meeting are called. This applies to both women and men. If it was only between these two, which one do you think seems more fair? | haven_labelled | 1. The first algorithm seems most fair, 2. The second algorithm seems most fair, 3. Do not know |
1 | 0.9975845 | 1 | 1 | 3 | 1.5956416 | 0.7265294 | 3 | ▇▁▁▅▁▁▁▂ | F40.0 | 5 |
| fg_kjennskap_prv | Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison? | haven_labelled | 1. Oppose very strongly, 2. Oppose strongly, 3. Oppose somewhat, 4. Neither oppose or support, 5. Support somewhat, 6. Support strongly, 7. Support very stringly, 8. Do not know |
1 | 0.9975845 | 1 | 4 | 8 | 3.9055690 | 1.7273620 | 8 | ▂▃▅▃▇▂▁▁ | F40.0 | 5 |
| fg_auto_prv | Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
1 | 0.9975845 | 1 | 1 | 3 | 1.1089588 | 0.3944298 | 3 | ▇▁▁▁▁▁▁▁ | F40.0 | 5 |
| fg_imp_1 | How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees | haven_labelled | 1. Not important at all, 2. Not important, 3. Somwhat important, 4. Very important, 5. Extremely improtant, 6. Do not know |
1 | 0.9975845 | 1 | 4 | 6 | 3.9661017 | 0.7719686 | 6 | ▁▁▁▃▇▁▃▁ | F40.0 | 5 |
| fg_imp_2 | How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting | haven_labelled | 1. Not important at all, 2. Not important, 3. Somwhat important, 4. Very important, 5. Extremely improtant, 6. Do not know |
1 | 0.9975845 | 1 | 4 | 6 | 3.8668281 | 0.7908388 | 6 | ▁▁▁▅▇▁▂▁ | F40.0 | 5 |
| fg_imp_3 | How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates | haven_labelled | 1. Not important at all, 2. Not important, 3. Somwhat important, 4. Very important, 5. Extremely improtant, 6. Do not know |
1 | 0.9975845 | 1 | 5 | 6 | 4.4406780 | 0.7237252 | 6 | ▁▁▁▁▆▁▇▁ | F40.0 | 5 |
| fg_flykt_post | Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
1 | 0.9975845 | 1 | 2 | 3 | 1.7191283 | 0.5561197 | 3 | ▅▁▁▇▁▁▁▁ | F40.0 | 5 |
| fg_nav_post | Case 2 of 3: Dialogue meeting with NAV As sick leave, you receive follow-up from NAV. They will assess whether you should be invited to a dialogue meeting to discuss what can be done to enable you to return to work. Dialogue meetings require resources, so not everyone can get that offer. When NAV assesses your need, you get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_di_treat] will take much more time than the algorithm, which extends the period accordingly before you possibly receive an invitation to a dialogue meeting. Which decision-making process would you have chosen? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
1 | 0.9975845 | 1 | 2 | 3 | 1.6222760 | 0.5934003 | 3 | ▇▁▁▇▁▁▁▁ | F40.0 | 5 |
| fg_prv_post | Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen? | haven_labelled | 1. Manual processing, 2. Algorithmic processing, 3. Do not know |
1 | 0.9975845 | 1 | 2 | 3 | 1.6246973 | 0.6661513 | 3 | ▇▁▁▇▁▁▁▂ | F40.0 | 5 |
| fg_tradeoff_1 | Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer? | haven_labelled | 1. A, 2. B, 3. Do not know |
1 | 0.9975845 | 1 | 1 | 3 | 1.6973366 | 0.7961788 | 3 | ▇▁▁▅▁▁▁▃ | F40.0 | 5 |
| fg_tradeoff_2 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two | haven_labelled | 1. X, 2. Y, 3. Do not know |
1 | 0.9975845 | 1 | 2 | 3 | 1.9661017 | 0.8583194 | 3 | ▇▁▁▆▁▁▁▇ | F40.0 | 5 |
| fg_tradeoff_3 | Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive | haven_labelled | 1. One, 2. Two, 3. Do not know |
2 | 0.9951691 | 1 | 2 | 3 | 1.9805825 | 0.7981802 | 3 | ▇▁▁▇▁▁▁▇ | F40.0 | 5 |
| demo_edu | What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you. | haven_labelled | 1. No eductation, 2. Pre-school, 3. High school, 5. Vocational school level (includes educations that are based on upper secondary school, but which are not approved as university and college education), 6. University or collage, up to 4 yearrs, 7. University or collage, more than 4 yearrs, 9. Other |
207 | 0.5000000 | 2 | 6 | 9 | 5.5603865 | 1.5718939 | 7 | ▁▅▁▃▇▇▁▁ | F40.0 | 5 |
| demo_party | Which party would you vote for if there were a parliamentary election tomorrow? | haven_labelled | 1. Progress party, 2. Conservative party, 3. Liberal Party, 4. Christian democrats, 13. Green party, 5. Center party, 6. Labour party, 7. Socialist party, 8. Red party, 9. Other, 10. Could not vote, 11. Would not vote |
208 | 0.4975845 | 1 | 6 | 13 | 6.1456311 | 3.6836031 | 12 | ▇▃▁▇▃▂▂▃ | F40.0 | 5 |
| demo_trust | How much trust or distrust do you have in scientists? | haven_labelled | 1. Very high trust, 2. High trust, 3. Some trust, 4. Neither trust nor mistrust, 5. Some mistrust, 6. High mistrust, 7. Very high mistrust, 8. Do not know |
207 | 0.5000000 | 1 | 2 | 7 | 2.3864734 | 1.0727927 | 8 | ▂▇▂▂▁▁▁▁ | F40.0 | 5 |
| demo_lr_scale | In politics, you often talk about the ‘left’ and the ‘right’. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale? | haven_labelled | 0. 0 - Left, 1. 1, 2. 2, 3. 3, 4. 4, 5. 5, 6. 6, 7. 7, 8. 8, 9. 9, 10. 10, 11. 11- Right |
208 | 0.4975845 | 0 | 5 | 11 | 5.1650485 | 2.7975498 | 12 | ▂▃▇▆▃▆▁▃ | F40.0 | 5 |
| post | 0 == Pre deliberation survey responses, 1 == post delibiration survey | numeric | . | 0 | 1.0000000 | 0 | 0.5 | 1 | 0.5000000 | 0.5006050 | NA | ▇▁▁▁▇ | NA | NA |
| fg_sikkert_post | Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation? | haven_labelled | 1. Veldig negativ, 2. Veldig negativ, 3. Veldig negativ |
297 | 0.2826087 | 1 | 3 | 3 | 2.6666667 | 0.5085476 | 3 | ▁▁▁▃▁▁▁▇ | F40.0 | 5 |
| fo_delib | Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts? | haven_labelled | 1. Veldig negativ, 2. Veldig negativ, 3. Veldig negativ, 4. Veldig negativ |
208 | 0.4975845 | 1 | 2 | 3 | 1.6262136 | 0.6097518 | 4 | ▇▁▁▇▁▁▁▁ | F40.0 | 5 |
| fo_media | Vi takker så mye for din deltakelse! |
Helt til slutt: Det kan hende noen media ønsker å komme i
kontakt med enkelte deltakere for å høre om deres opplevelser med å
delta i dette arrangementet. Dersom det skulle komme en slik
henvendelse, vil du være villig til at de tar kontakt med deg?
Svaret ditt er ikke bindende. |haven_labelled |4. Veldig
negativ,
5. Veldig negativ | 209| 0.4951691|4 |4 |5 | 4.3463415|
0.4769683| 2|▇▁▁▁▁▁▁▅ |F40.0 |5 |
|on_sikkert_post |Føler du deg mer sikker
eller mer usikker i ditt syn på ulike metoder for å fjerne CO2 fra
luften etter at du deltok i deliberasjonen? |haven_labelled |1. Veldig
negativ,
2. Veldig negativ,
3. Veldig negativ | 325| 0.2149758|1
|3 |3 | 2.5280899| 0.6757132| 3|▁▁▁▃▁▁▁▇ |F40.0 |5 |
|fg |1 == Treatment group (AI), 0 == control group
(carbon capture) |numeric |. | 0| 1.0000000|0 |1.0 |1 | 0.5700483|
0.4956679| NA|▆▁▁▁▇ |NA |NA |
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": ".",
"datePublished": "2022-11-16",
"description": "The dataset has N=414 rows and 35 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
"keywords": ["sikker_fg", "fg_kunnskap_1", "fg_kunnskap_2", "fg_kunnskap_3", "fg_forbedring", "fg_likhet_1", "fg_likhet_2", "fg_likhet_3", "fg_kunnskap2", "fg_auto_flykt", "fg_region", "fg_kjennskap_nav", "fg_auto_nav", "fg_pari_nav", "fg_kjennskap_prv", "fg_auto_prv", "fg_imp_1", "fg_imp_2", "fg_imp_3", "fg_flykt_post", "fg_nav_post", "fg_prv_post", "fg_tradeoff_1", "fg_tradeoff_2", "fg_tradeoff_3", "demo_edu", "demo_party", "demo_trust", "demo_lr_scale", "post", "fg_sikkert_post", "fo_delib", "fo_media", "on_sikkert_post", "fg"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "sikker_fg",
"description": "Are you sure that you do not consent to our data processing and want to end the survey? You cannot then participate in the event on 11 June either, as the consent also applies to that event.",
"value": "1. Jeg vil delta i undersøkelsen,\n2. Jeg samtykker ikke og vil ikke delta i undersøkelsen eller det påfølgende arrangementet",
"maxValue": 2,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap_1",
"description": "First, how well would you say you have the following knowledge: - Machine learning and artificial intelligence",
"value": "1. Very good knowledge,\n2. Good knowledge,\n3. Somwhat good knowledge,\n4. Little knowledge,\n5. No knowledge at all,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap_2",
"description": "First, how well would you say you know the following: - How the Norwegian authorities currently decide where refugees will be settled once they have been granted residence in Norway",
"value": "1. Very good knowledge,\n2. Good knowledge,\n3. Somwhat good knowledge,\n4. Little knowledge,\n5. No knowledge at all,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap_3",
"description": "First, how well would you say you know the following: - How NAV currently decides which people on sick leave should be invited to a dialogue meeting about how they can more easily return to work",
"value": "1. Very good knowledge,\n2. Good knowledge,\n3. Somwhat good knowledge,\n4. Little knowledge,\n5. No knowledge at all,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_forbedring",
"description": "Based on what you know, do you think the Norwegian authorities' use of machine learning and artificial intelligence will lead to an improvement or worsening of public services?",
"value": "1. Very strong improvement,\n2. Strong improvement,\n3. Some improvement,\n4. Neither improvement or worsening,\n5. Some worsening,\n6. Strong worsening,\n7. Very strong worsening,\n9. Do not know",
"maxValue": 9,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_likhet_1",
"description": "And what do you think it will lead to in terms of the following points? - Verifiability: Possibility of getting an explanation of the decision for those who are affected by it",
"value": "1. Very much more,\n2. More,\n3. Somwhat more,\n4. Neither more or less,\n6. Less,\n7. Very much less,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_likhet_2",
"description": "And what do you think it will lead to in terms of the following points? - Impartiality: Possibility for citizens to be treated equally by the public sector",
"value": "1. Very much more,\n2. More,\n3. Somwhat more,\n4. Neither more or less,\n5. Somewhat less,\n6. Less,\n7. Very much less,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_likhet_3",
"description": "And what do you think it will lead to in terms of the following points? - Legitimacy: Likelihood that the citizens will voluntarily follow the authorities' decisions",
"value": "1. Very much more,\n2. More,\n3. Somwhat more,\n4. Neither more or less,\n5. Somewhat less,\n6. Less,\n7. Very much less,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kunnskap2",
"description": "Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of algorithms based on machine learning and artificial intelligence when settling refugees?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_auto_flykt",
"description": "Imagine two situations. In one situation, a case manager will settle refugees based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved in the case (fully automated procedure). Which of these two procedures do you prefer?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n4. Do not know",
"maxValue": 4,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_region",
"description": "Algorithms that are developed based on machine learning and artificial intelligence need large amounts of data to be able to create accurate models for the settlement of refugees. It then becomes difficult to create algorithms that are accurate enough for each individual municipality, since there is too little basis on which to base the model on. An alternative is to merge larger areas into labor market regions, and request the settlement of refugees according to these regions. How strongly do you support or oppose requests for the settlement of refugees being made by region instead of municipalities?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kjennskap_nav",
"description": "Based on your knowledge, to what extent do you support that NAV should be open to using artificial intelligence when deciding who should be invited to a dialogue meeting?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_auto_nav",
"description": "Imagine two situations. In one situation, a case manager will invite to a dialogue meeting based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_pari_nav",
"description": "Machine learning involves getting computers to learn to solve tasks based on data material. A dilemma that often arises is that different algorithms have different advantages and disadvantages, and you have to choose which considerations to prioritize. Imagine that NAV has to choose between using one of these two algorithms: The first algorithm is most accurate. Among those who need a dialogue meeting, however, more [Field-fairgov_pari_treat] are offered a dialogue meeting, even though there are an equal number of women and men who are on sick leave and need a dialogue meeting. The proportion who need a dialogue meeting without receiving an offer is therefore larger at [Field-fairgov_pari_treat_2]. The second algorithm ensures that the proportion of those on sick leave who are called to a dialogue meeting is equal for women and men. However, it is less accurate overall, so that fewer people who need a dialogue meeting are called. This applies to both women and men. If it was only between these two, which one do you think seems more fair?",
"value": "1. The first algorithm seems most fair,\n2. The second algorithm seems most fair,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_kjennskap_prv",
"description": "Based on your knowledge, how strongly do you support or oppose the Norwegian authorities opening up the use of artificial intelligence when deciding who should be paroled from prison?",
"value": "1. Oppose very strongly,\n2. Oppose strongly,\n3. Oppose somewhat,\n4. Neither oppose or support,\n5. Support somewhat,\n6. Support strongly,\n7. Support very stringly,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_auto_prv",
"description": "Imagine two situations. In one situation, a case manager will grant a prison inmate parole based on his own assessment with the support of an algorithm (semi-automated procedure). In the second situation, it is the same algorithm that makes the decision completely alone, without a case manager being involved (fully automated procedure). Which of these two procedures do you prefer?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_imp_1",
"description": "How important do you think it is that the authorities come to the right decisions in the following matters: - Settlement of refugees",
"value": "1. Not important at all,\n2. Not important,\n3. Somwhat important,\n4. Very important,\n5. Extremely improtant,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_imp_2",
"description": "How important do you think it is that the authorities come to the right decisions in the following matters: - Calling for a dialogue meeting",
"value": "1. Not important at all,\n2. Not important,\n3. Somwhat important,\n4. Very important,\n5. Extremely improtant,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_imp_3",
"description": "How important do you think it is that the authorities come to the right decisions in the following cases: - Parole of prison inmates",
"value": "1. Not important at all,\n2. Not important,\n3. Somwhat important,\n4. Very important,\n5. Extremely improtant,\n6. Do not know",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_flykt_post",
"description": "Case 1 of 3: Settlement of refugees As a refugee to Norway, you have been granted asylum, and are waiting in an asylum reception center to be settled in a municipality. You get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_bo_treat] will take much more time than the algorithm, which will extend your stay at the asylum reception accordingly. Which decision-making process would you have chosen?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_nav_post",
"description": "Case 2 of 3: Dialogue meeting with NAV As sick leave, you receive follow-up from NAV. They will assess whether you should be invited to a dialogue meeting to discuss what can be done to enable you to return to work. Dialogue meetings require resources, so not everyone can get that offer. When NAV assesses your need, you get the choice between having your case assessed manually by a case manager or fully automatically using an algorithm. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_di_treat] will take much more time than the algorithm, which extends the period accordingly before you possibly receive an invitation to a dialogue meeting. Which decision-making process would you have chosen?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_prv_post",
"description": "Case 3 of 3: Parole As an inmate, you have applied for parole, and are given the choice between having your case assessed manually or automatically. You can expect the case manager to take longer than the algorithm to assess your case. Normally, [Field-fairgov_manvsmach_pr_treat] will take much more time than the algorithm, which extends the period accordingly before you can possibly be paroled. Which decision-making process would you have chosen?",
"value": "1. Manual processing,\n2. Algorithmic processing,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_tradeoff_1",
"description": "Imagine that you have to choose between machine learning models A and B that differ by the characteristics shown in the figure below. The characteristics of Model A are shown by the two columns on the left, and the characteristics of Model B are shown by the two columns on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole. Model A has a lower proportion of wrong decisions (33%) than Model B (66%). Model B has a lower proportion of incorrect rejections (33%) than Model A (66%). Which of these two do you perceive to be fairer?",
"value": "1. A,\n2. B,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_tradeoff_2",
"description": "Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models X and Y that differ in the characteristics shown in the figure below. The features of Model X are shown on the left and the features of Model Y are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.False denial rate: How often the model is expected to deny parole to someone who should actually be granted parole.Model X misses equally for black and white Americans, but falsely rejects a greater share of whites (45%) than blacks (24%).Model Y falsely rejects an equal share of white and black Americans, but misses more whites (47%) than blacks (21%). Which of these two",
"value": "1. X,\n2. Y,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg_tradeoff_3",
"description": "Algorithm-based parole has been used in the United States. Some believe that this difference between blacks and whites should be equalized in their chance of being paroled, while others believe that everyone should receive the same process, regardless of origin.Imagine that you have to choose between machine learning models 1 and 2 which differ in the characteristics shown in the figure below. The characteristics of Model 1 are shown on the left and the characteristics of Model 2 are shown on the right. Rate of wrong decisions: How often the model is expected to miss a decision. A lower number means it is more accurate.Proportion that gets parole: What proportion will get parole from the model. Model 1 has an equal share of wrongful convictions between black and white Americans, but grants parole to more whites (55%) than blacks (34%). Model 2 has a greater proportion of wrongful convictions for white (47%) than black (21%) Americans, but grants parole to an equal proportion in each group. Which of these two do you perceive",
"value": "1. One,\n2. Two,\n3. Do not know",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_edu",
"description": "What is your highest completed education? If you have several educations at the same level, choose the one that is most relevant to you.",
"value": "1. No eductation,\n2. Pre-school,\n3. High school,\n5. Vocational school level (includes educations that are based on upper secondary school, but which are not approved as university and college education),\n6. University or collage, up to 4 yearrs,\n7. University or collage, more than 4 yearrs,\n9. Other",
"maxValue": 9,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_party",
"description": "Which party would you vote for if there were a parliamentary election tomorrow?",
"value": "1. Progress party,\n2. Conservative party,\n3. Liberal Party,\n4. Christian democrats,\n13. Green party,\n5. Center party,\n6. Labour party,\n7. Socialist party,\n8. Red party,\n9. Other,\n10. Could not vote,\n11. Would not vote",
"maxValue": 13,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_trust",
"description": "How much trust or distrust do you have in scientists?",
"value": "1. Very high trust,\n2. High trust,\n3. Some trust,\n4. Neither trust nor mistrust,\n5. Some mistrust,\n6. High mistrust,\n7. Very high mistrust,\n8. Do not know",
"maxValue": 8,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "demo_lr_scale",
"description": "In politics, you often talk about the 'left' and the 'right'. Below is a scale where 0 represents those who are on the far left politically, and 10 represents those who are on the far right politically. How would you place yourself on such a scale?",
"value": "0. 0 - Left,\n1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7,\n8. 8,\n9. 9,\n10. 10,\n11. 11- Right",
"maxValue": 11,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "post",
"description": "0 == Pre deliberation survey responses, 1 == post delibiration survey",
"value": ". ",
"maxValue": "-Inf",
"minValue": "Inf",
"@type": "propertyValue"
},
{
"name": "fg_sikkert_post",
"description": "Do you feel more confident or more uncertain in your view of the use of artificial intelligence in administration after you participated in the deliberation?",
"value": "1. Veldig negativ,\n2. Veldig negativ,\n3. Veldig negativ",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fo_delib",
"description": "Now that you have participated in this deliberation, we want to know what you think about including ordinary citizens in political discussions on this kind of topic. Do you think that ordinary people have a lot of value to contribute and that politicians and experts should listen to this, or do you think that ordinary people have nothing valuable to contribute and that the discussions should be left to politicians and experts?",
"value": "1. Veldig negativ,\n2. Veldig negativ,\n3. Veldig negativ,\n4. Veldig negativ",
"maxValue": 4,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fo_media",
"description": "Vi takker så mye for din deltakelse! \r\n\r\nHelt til slutt: Det kan hende noen media ønsker å komme i kontakt med enkelte deltakere for å høre om deres opplevelser med å delta i dette arrangementet. Dersom det skulle komme en slik henvendelse, vil du være villig til at de tar kontakt med deg? \r\n\r\nSvaret ditt er ikke bindende.",
"value": "4. Veldig negativ,\n5. Veldig negativ",
"maxValue": 5,
"minValue": 4,
"@type": "propertyValue"
},
{
"name": "on_sikkert_post",
"description": "Føler du deg mer sikker eller mer usikker i ditt syn på ulike metoder for å fjerne CO2 fra luften etter at du deltok i deliberasjonen?",
"value": "1. Veldig negativ,\n2. Veldig negativ,\n3. Veldig negativ",
"maxValue": 3,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "fg",
"description": "1 == Treatment group (AI), 0 == control group (carbon capture)",
"value": ". ",
"maxValue": "-Inf",
"minValue": "Inf",
"@type": "propertyValue"
}
]
}`